# Discovery of Product Features for Redesign from User Implicit Feedback

Authors: Xiaolong Wang; Yu Wang; Jiacong Wu; Sara Shafiee
Journal: Mobile Information Systems
 (2022)

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## Abstract

Online reviews are key sources to help manufacturers design and improve their products. However, using online reviews for product redesign is challenging because many online reviews contain useless information, and customers rarely provide explicit information about the products. The few methods available to recognize the customers’ demands from online reviews have several limitations, including failing to consider the redundancy of useless reviews, confusion in providing targeted information among different aspects of product design, and disregarding the manufacturing limitation. Hence, considering customer needs and manufacturing constraints for product improvement, this study proposes a new model called DPFR to discover the potential product features for redesign. In DPFR, it first analyzes and predicts helpfulness of reviews for more effective decision of product design and improvement, which is neglected in previous research. Then, a structured model based on topic modeling is conducted. Overlooked by previous researchers, DPFR also introduces a feature importance matric to measure the significance of each commented feature to product redesign. Third, a target features selection model is conducted further considering the limitations of manufacturers rather than only focusing on customer satisfaction. The effectiveness of the proposed model was assessed using a case study of data acquired from Bilibili, a large short-video platform in China. Focusing only on customer satisfaction, the results confirm the effectiveness of DPFR. Finally, including the constraints of product redesign, DPFR balances customer satisfaction and the manufacturing constraints such as redesign cost, redesign time, and carbon emissions. This study provides more practical suggestions for manufacturers to discover product redesign features that result in strategic product improvement decisions.


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## Body

## 1. Introduction

Following a dynamic market environment, redesign is widely required to improve the existing products [1].Most product improvements are based on the redesign of existing products [2]. In the product design and improvement process, according to a survey, 70% of manufacturers choose to redesign rather than design from scratch [1]. In the process of product redesign, it is vital to meet customers’ needs by discovering the product features for the redesign [3, 4]. Identifying potential product features for redesign can help manufacturers formulate product improvement strategies [5].Researchers and practitioners have investigated the identification of product features for redesign. However, most previous works relied on surveys and interviews to identify the product features for the redesign [5–7]. Guided by the questions in the surveys and interviews, customers can express their demands and preferences explicitly. However, surveys and interviews require a significant investment in manpower and resources [6]. In recent years, with the rapid development of information technology, the social media developed rapidly. We have witnessed a convenient platform for customers to share opinions and experiences about the products. Furthermore, online reviews about products have also become an essential source for manufacturers to understand the customers’ demands [8–11].Considering recent progress in social media, researchers have used online reviews to identify product features for the redesign [5, 7, 12]. Although online reviews conveniently allow customers to express their opinions about products, customers rarely provide explicit information. Thus, the product features suitable for the redesign are hidden in these online reviews [13]. The primary challenge is finding an effective solution to utilize online reviews for product redesign. Several prior studies have focused on mining product features for redesign. Conjoint analysis with the KANO model is a widely used method for identifying potential product features for redesign [5, 8, 12, 14]. The goal of conjoint analysis is to determine the weight of all potential product features eligible for the redesign. The KANO model is used to measure the level of importance of potential product features [15, 16]. Nevertheless, conjoint analysis still has limitations. Hence, the main gaps in the current literature are discussed below.First, online reviews contain a lot of useless information, which brings redundancy to the process of selecting potential product features for redesign. However, high-quality online reviews positively affect the consumer purchase intentions [17]. Manufacturers are therefore challenged with capturing customer feedback from helpful online reviews to improve product design [18]. However, previous studies have neglected how helpful reviews influence decisions surrounding mining product features for redesigns. Second, online reviews present different product topics discussed by customers. Considering online reviews of electronic products as an example, these reviews usually include different product features, such as CPU, speaker, screen, and battery, which belong to the hardware dimension of the product. In this paper, the different dimensions of products are called product topics. The product topics can be identified by the topic modeling of online reviews but have been neglected by the researchers. In selecting the potential features for the redesign, the level of importance or weight of each product topic also needs to be considered [4]. Third, previous studies have focused on selecting product features for redesign based on online reviews only to meet the customer satisfaction factors [12, 19]. However, focusing on product redesign, we must consider not only the customer needs but also the constraints of product redesign from the manufacturers’ perspective.To overcome the limitations of existing studies, this study aims to answer the following research questions:(1)
How can we benefit from helpful reviews to explore potential product features that are suitable for the redesign?(2)
How can we extract the customers’ needs based on the helpful reviews on theproduct topics?(3)
How can we construct a model to select product features for redesign, considering both the customer needs and the limitations of manufacturers?To address the above questions, this paper presents the DPFR method to identify potential product features for the redesign. The DPFR consists of three models. The first model discovers helpful online reviews from the manufacturers’ perspective. Second, a structured model based on topic modeling is constructed to cluster helpful online reviews into differentproduct topics and calculate the importance of each topic. Hence, an index is subsequently introduced from the structured model to measure the priority of all potential product features for the redesign. Finally, the product features selection model for the redesign is constructed, considering both customers’ needs and the constraints of product redesign.The contributions of this study can be summarized as follows. First, this study explains how helpful reviews contribute to product redesign and improvement. Second, this study investigates the influence ofproduct topics that the customers discuss on social media. Third, this study considers not only customer satisfaction but also product redesign constraints, which help to develop product redesign strategies. Fourth, DPFR can be used as a real-time customer monitoring tool to help product designers cope with the rapid changes in customers’ needs and formulate plans for product redesign in time.The rest of the paper is organized as follows: The next section briefly describes the related works. Section3 introduces the proposed method for identifying product features for the redesign. Section 4 uses the case study of Huawei MatePad 11 and the data acquired from Bilibili to verify the feasibility and validity of the proposed method. Finally, the discussion and conclusions are presented in Section 5.


## 2. Related Works

### 2.1. Identification of Helpful Online Reviews

In the Web 2.0 era, customers can share their product experiences and opinions online. A product can easily receive hundreds or even thousands of online reviews from customers ([20–22]. Thereafter, the reviews provide valuable feedback and insights from the customers’ perspective for product manufacturers and service providers, enabling them to make sustainable decisions on product innovation and improvements [5]. However, online reviews also contain useless reviews that have nothing to do with product design and functionality. Therefore, it is particularly important to identify helpful product reviews [18, 23]. Previous studies have used various features extracted from customer reviews and classification models to identify helpful reviews, as summarized in Table 1. The classification models used in Table 1 include an artificial neural network, support vector machine (SVM), empirical analysis, naive Bayes (NB), random forest (RF), and K-nearest neighbor (KNN).Table 1
Summary of existing studies for identifying helpful reviews.
CategoriesFeaturesLiteraturesLengthNumber of wordsMartin and Pu [24]; Eslami et al. [25]; Luo et al. [26]; Meng et al. [23]Number of sentencesKim et al. [27]; Martin and Pu [24]; Zeenia Singla and Sushma [28]; Eslami et al. [25]; Meng et al. [23]; Lutz et al. [29]SentimentPositive wordsCao et al. [30]; Chua and Banerjee [31]; Zeenia Singla and Sushma [28]; Liu et al. [32]; Oumayma Oueslati and Sushma [33]; Zeng et al. [34]; Chua and Chen [35]Negative wordsCao et al. [30]; Chua and Banerjee [31]; Zeenia Singla and Sushma [28]; Liu et al. [32]; Oumayma Oueslati and Sushma [33]; Zeng et al. [34]; Chua and Chen [35]Number of positive wordsKaushik et al. [36]; Li et al. [37]Number of negative wordsKaushik et al. [36]; Li et al. [37]LexicalNumber of nounsKim et al. [27]; Kikuchi and Klyuev [38]; Krishnamoorthy [39]; Jiang et al. [18]; Du et al. [40]; Shan et al. [41]Number of verbsKim et al. [27]; Krishnamoorthy [39]; Jiang et al. [18]; Du et al. [40]; Shan et al. [41]Number of adverbsKim et al. [27]; Krishnamoorthy [39]; Jiang et al. [18]; Du et al. [40]; Shan et al. [41]Number of adjectivesKim et al. [27]; Kikuchi and Klyuev [38]; Krishnamoorthy [39]; Jiang et al. [18]; Du et al. [40]; Shan et al. [41]The length of a review is a highly influential review factor [29]. The majority of the studies have proven that the length of a review has a positive effect on predicting the level of the review’s helpfulness [24–26, 29]. To automatically evaluate the usefulness of online reviews, Kim et al. [27] selected features (e.g., punctuation, review length, and star rating) from Amazon.com product reviews. Then, the support vector regression was trained to learn the helpfulness function, subsequently identifying the length of a review as one of the most useful features. Eslami et al. [25] used an artificial neural network to predict the helpfulness of reviews based on review length, review score, and argument frame. The results showed that review length was the most helpful factor in predicting the helpfulness of reviews. In order to select helpful reviews in user-generated content platforms, Luo et al. [26] used empirical studies to explore the moderating effect of reviews features on review helpfulness based on a large-scale online reviews data. Hereby, the results showed that the review length has a significant effect on review helpfulness. Lutz et al. [29] developed a model to test the validity of review length for identifying helpful reviews, and the results showed that the review length was useful for predicting helpful reviews with a fixed emotional orientation.Sentiment features are another influential factor that is strongly correlated with review helpfulness prediction [33, 34, 42, 43]. Sentiment features reveal the customer experience of products, mainly including negative, positive, and neutral emotional tendencies [30, 34]. Through the analysis of negative and positive features, manufacturers can evaluate customer needs and preferences about their products [28, 33]. Through empirical research, Cao et al. [30] examined the influence of various features (e.g., sentiment features) on review helpfulness and concluded that reviews with positive and negative features are more useful than those with mixed or neutral features. Chua and Banerjee [31] classified sentiment features into positive, negative, and mixed. They indicated that positive features are more related to helpful votes. Zeng et al. [34] examined the impact of sentiment features and product type on review helpfulness predication, and the results revealed that sentiment features are more useful than the product type. Chua and Chen [35] used empirical studies to explore the relationship between useful and false reviews, and the results suggested that sentiment features can help to distinguish helpful false reviews from helpful authentic reviews.Together with review length and sentiment features, studies have identified that lexical features have an effect on review helpfulness [41, 44, 45]. Nouns, verbs, adverbs, and adjectives are widely used as lexical features [18, 27, 38–40, 46]. Krishnamoorthy [39] used the NLTK Parts-of-Speech (POS) tagger to tag each customer review and reported that lexical features are quite effective in predicting review helpfulness. Jiang et al. [18] proposed a multi-class classification approach based on ensemble learning to identify helpful reviews and verified that the number of lexical features is an important factor in determining the classifier performance. Du et al. [40] selected 30 different features from relevant research papers to test the performance of different feature combination patterns in helpful review predications. They suggested that the feature combination based on nouns, verbs, adverbs, and adjectives leads to visible performance. Shan et al. [41] constructed a machine learning method based on 22 features to identify fake reviews and analyzed the importance of different features, and the results showed that nouns, verbs, and adjectives are important features to identify the fake reviews.


### 2.2. Topic Modeling

Topic modeling is a text-mining method for topic discovery [47, 48]. Researchers have applied topic modeling in various fields (e.g., online review analysis and medical sciences) [47, 49–51]. Topic modeling based on latent Dirichlet allocation (LDA) is widely used in online review analysis to find business opportunities, such as extracting service quality dimensions [49], identifying customer satisfaction dimensions [12, 52], and identifying latent product topics discussed by customers [4, 53, 54]. To measure healthcare service quality, James et al. [55] proposed a text-mining method to analyze large amounts of unstructured patient feedback using the LDA clustering model. They verified that the topic areas extracted by LDA are useful in evaluating physician performance based on feedback. Nilashi et al. [52] proposed a combined method based on LDA, TOPSIS, and neural networks for customer segmentation in green hotels. LDA was used to extract different aspects of customer satisfaction. The results showed that LDA is effective in discovering a mixture of topics from online reviews. Jeong et al. [4] presented an approach based on LDA and sentiment analysis to mine opportunities for further product improvement and highlighted the importance of topics discussed by the customers. In order to get the preference of online game community players, Yu et al. [54] used LDA to analyze the reviews collected from Steam game community, and finally obtained the players’ favorite topics.


### 2.3. Identification of Product Features from Online Reviews

The primary task of product redesign is to capture redesign requirements (e.g., features that need to be improved), which is an important research area for improving product quality and reducing product costs [56, 57]. Traditional product redesigns are mainly based on quantitative, qualitative, or hybrid methods to discover customers’ needs, using interviews, laboratory research, and market research [58–61]. These methods often require labor-intensive work, such as organizing tests, finding suitable participants, designing appropriate questionnaires, and testing products [7]. Although traditional methods have proven to be effective, they have limitations due to the small sample size and high costs [6].With the rapid growth of big data, online reviews have become an important data source for product redesign and innovation in rapidly changing market [11, 19, 62]. Analyzing customer needs and preferences, online reviews have been used in many studies to discover to-be-improved features. The majority of studies have focused on discovering product features that have a great effect on customer satisfaction, as shown in Table 2. Conjoint analysis with the KANO model has been widely used in previous studies [5, 8, 12, 14]. The goal of conjoint analysis is to determine the weight of all potential product features for the redesign [65]. Sentiment analysis is a widely used method in conjoint analysis that calculates the customer satisfaction level of each product feature. The KANO model is then applied to develop suggested product improvement strategies [15]. In general, potential product features are classified into five KANO categories: attractive, one-dimensional, must-be, indifferent, and reverse. Martínez-Torres [66] proposed a method based on conjoint analysis and KANO to extract customer changing expectations for product affordances. Moreover, Wu et al. [16] proposed a framework to discover the priority of product/service improvement based on online reviews by using semantic analysis and the KANO model to capture products/services features. In the framework, sentiment analysis was used to calculate the performance of the features, and then the KANO model was used to measure the importance of each feature’s performance. Finally, product features were classified into attractive, one-dimensional, must-be, and indifferent in their study. They reported that their method is effective in analyzing customers’ needs. Bi et al. [12] used the KANO model to identify different customer satisfaction dimensions, and the results showed that manufacturers mainly focus on one-dimensional and attractive categories for product improvement process. Joung and Kim [14] used the KANO model to rank products from the customers’ perspective, and the results showed that the KANO model could recognize different types of customer preferences.Table 2
Summary of existing research to identify the product features for redesign based on focused areas.
Suggested models/methodsFocused areasCustomer satisfactionThe constraints of product improvementQi et al. [5]√×Wu et al. [16]√×Bi et al. [12]√×Hou et al. [8]√×Yang et al. [7]√×Zhou et al. [63]√×Jin et al. [64]√×Joung and Kim [14]√×Lee et al. [15]√×This study√√As presented, previous studies have discovered that the quality of online reviews is vital for both customers and product manufacturers. Many studies have investigated the key effective features of identifying helpful reviews. However, these studies did not explain how helpful reviews can benefit strategic decisions for the product improvement.Investigating the potential product features, the majority of researchers focused on mining customer preferences and opinions about products from customers’ perspectives by calculating the customer satisfaction level of the product features. However, many studies have neglected the effects ofproduct topics. Furthermore, the literature lacks a method for extracting and using customer preferences and opinions about products. Focusing on product redesign, manufacturers must not only consider the customers’ need but also the constraints of product redesign.Accordingly, this study proposes a novel method that aims to discover the product features from users’ implicit feedbacks for the product redesign. The research results have broad impacts on product redesign and product improvement strategies using the analytics of online reviews and capturing customer needs.


## 2.1. Identification of Helpful Online Reviews

In the Web 2.0 era, customers can share their product experiences and opinions online. A product can easily receive hundreds or even thousands of online reviews from customers ([20–22]. Thereafter, the reviews provide valuable feedback and insights from the customers’ perspective for product manufacturers and service providers, enabling them to make sustainable decisions on product innovation and improvements [5]. However, online reviews also contain useless reviews that have nothing to do with product design and functionality. Therefore, it is particularly important to identify helpful product reviews [18, 23]. Previous studies have used various features extracted from customer reviews and classification models to identify helpful reviews, as summarized in Table 1. The classification models used in Table 1 include an artificial neural network, support vector machine (SVM), empirical analysis, naive Bayes (NB), random forest (RF), and K-nearest neighbor (KNN).Table 1
Summary of existing studies for identifying helpful reviews.
CategoriesFeaturesLiteraturesLengthNumber of wordsMartin and Pu [24]; Eslami et al. [25]; Luo et al. [26]; Meng et al. [23]Number of sentencesKim et al. [27]; Martin and Pu [24]; Zeenia Singla and Sushma [28]; Eslami et al. [25]; Meng et al. [23]; Lutz et al. [29]SentimentPositive wordsCao et al. [30]; Chua and Banerjee [31]; Zeenia Singla and Sushma [28]; Liu et al. [32]; Oumayma Oueslati and Sushma [33]; Zeng et al. [34]; Chua and Chen [35]Negative wordsCao et al. [30]; Chua and Banerjee [31]; Zeenia Singla and Sushma [28]; Liu et al. [32]; Oumayma Oueslati and Sushma [33]; Zeng et al. [34]; Chua and Chen [35]Number of positive wordsKaushik et al. [36]; Li et al. [37]Number of negative wordsKaushik et al. [36]; Li et al. [37]LexicalNumber of nounsKim et al. [27]; Kikuchi and Klyuev [38]; Krishnamoorthy [39]; Jiang et al. [18]; Du et al. [40]; Shan et al. [41]Number of verbsKim et al. [27]; Krishnamoorthy [39]; Jiang et al. [18]; Du et al. [40]; Shan et al. [41]Number of adverbsKim et al. [27]; Krishnamoorthy [39]; Jiang et al. [18]; Du et al. [40]; Shan et al. [41]Number of adjectivesKim et al. [27]; Kikuchi and Klyuev [38]; Krishnamoorthy [39]; Jiang et al. [18]; Du et al. [40]; Shan et al. [41]The length of a review is a highly influential review factor [29]. The majority of the studies have proven that the length of a review has a positive effect on predicting the level of the review’s helpfulness [24–26, 29]. To automatically evaluate the usefulness of online reviews, Kim et al. [27] selected features (e.g., punctuation, review length, and star rating) from Amazon.com product reviews. Then, the support vector regression was trained to learn the helpfulness function, subsequently identifying the length of a review as one of the most useful features. Eslami et al. [25] used an artificial neural network to predict the helpfulness of reviews based on review length, review score, and argument frame. The results showed that review length was the most helpful factor in predicting the helpfulness of reviews. In order to select helpful reviews in user-generated content platforms, Luo et al. [26] used empirical studies to explore the moderating effect of reviews features on review helpfulness based on a large-scale online reviews data. Hereby, the results showed that the review length has a significant effect on review helpfulness. Lutz et al. [29] developed a model to test the validity of review length for identifying helpful reviews, and the results showed that the review length was useful for predicting helpful reviews with a fixed emotional orientation.Sentiment features are another influential factor that is strongly correlated with review helpfulness prediction [33, 34, 42, 43]. Sentiment features reveal the customer experience of products, mainly including negative, positive, and neutral emotional tendencies [30, 34]. Through the analysis of negative and positive features, manufacturers can evaluate customer needs and preferences about their products [28, 33]. Through empirical research, Cao et al. [30] examined the influence of various features (e.g., sentiment features) on review helpfulness and concluded that reviews with positive and negative features are more useful than those with mixed or neutral features. Chua and Banerjee [31] classified sentiment features into positive, negative, and mixed. They indicated that positive features are more related to helpful votes. Zeng et al. [34] examined the impact of sentiment features and product type on review helpfulness predication, and the results revealed that sentiment features are more useful than the product type. Chua and Chen [35] used empirical studies to explore the relationship between useful and false reviews, and the results suggested that sentiment features can help to distinguish helpful false reviews from helpful authentic reviews.Together with review length and sentiment features, studies have identified that lexical features have an effect on review helpfulness [41, 44, 45]. Nouns, verbs, adverbs, and adjectives are widely used as lexical features [18, 27, 38–40, 46]. Krishnamoorthy [39] used the NLTK Parts-of-Speech (POS) tagger to tag each customer review and reported that lexical features are quite effective in predicting review helpfulness. Jiang et al. [18] proposed a multi-class classification approach based on ensemble learning to identify helpful reviews and verified that the number of lexical features is an important factor in determining the classifier performance. Du et al. [40] selected 30 different features from relevant research papers to test the performance of different feature combination patterns in helpful review predications. They suggested that the feature combination based on nouns, verbs, adverbs, and adjectives leads to visible performance. Shan et al. [41] constructed a machine learning method based on 22 features to identify fake reviews and analyzed the importance of different features, and the results showed that nouns, verbs, and adjectives are important features to identify the fake reviews.


## 2.2. Topic Modeling

Topic modeling is a text-mining method for topic discovery [47, 48]. Researchers have applied topic modeling in various fields (e.g., online review analysis and medical sciences) [47, 49–51]. Topic modeling based on latent Dirichlet allocation (LDA) is widely used in online review analysis to find business opportunities, such as extracting service quality dimensions [49], identifying customer satisfaction dimensions [12, 52], and identifying latent product topics discussed by customers [4, 53, 54]. To measure healthcare service quality, James et al. [55] proposed a text-mining method to analyze large amounts of unstructured patient feedback using the LDA clustering model. They verified that the topic areas extracted by LDA are useful in evaluating physician performance based on feedback. Nilashi et al. [52] proposed a combined method based on LDA, TOPSIS, and neural networks for customer segmentation in green hotels. LDA was used to extract different aspects of customer satisfaction. The results showed that LDA is effective in discovering a mixture of topics from online reviews. Jeong et al. [4] presented an approach based on LDA and sentiment analysis to mine opportunities for further product improvement and highlighted the importance of topics discussed by the customers. In order to get the preference of online game community players, Yu et al. [54] used LDA to analyze the reviews collected from Steam game community, and finally obtained the players’ favorite topics.


## 2.3. Identification of Product Features from Online Reviews

The primary task of product redesign is to capture redesign requirements (e.g., features that need to be improved), which is an important research area for improving product quality and reducing product costs [56, 57]. Traditional product redesigns are mainly based on quantitative, qualitative, or hybrid methods to discover customers’ needs, using interviews, laboratory research, and market research [58–61]. These methods often require labor-intensive work, such as organizing tests, finding suitable participants, designing appropriate questionnaires, and testing products [7]. Although traditional methods have proven to be effective, they have limitations due to the small sample size and high costs [6].With the rapid growth of big data, online reviews have become an important data source for product redesign and innovation in rapidly changing market [11, 19, 62]. Analyzing customer needs and preferences, online reviews have been used in many studies to discover to-be-improved features. The majority of studies have focused on discovering product features that have a great effect on customer satisfaction, as shown in Table 2. Conjoint analysis with the KANO model has been widely used in previous studies [5, 8, 12, 14]. The goal of conjoint analysis is to determine the weight of all potential product features for the redesign [65]. Sentiment analysis is a widely used method in conjoint analysis that calculates the customer satisfaction level of each product feature. The KANO model is then applied to develop suggested product improvement strategies [15]. In general, potential product features are classified into five KANO categories: attractive, one-dimensional, must-be, indifferent, and reverse. Martínez-Torres [66] proposed a method based on conjoint analysis and KANO to extract customer changing expectations for product affordances. Moreover, Wu et al. [16] proposed a framework to discover the priority of product/service improvement based on online reviews by using semantic analysis and the KANO model to capture products/services features. In the framework, sentiment analysis was used to calculate the performance of the features, and then the KANO model was used to measure the importance of each feature’s performance. Finally, product features were classified into attractive, one-dimensional, must-be, and indifferent in their study. They reported that their method is effective in analyzing customers’ needs. Bi et al. [12] used the KANO model to identify different customer satisfaction dimensions, and the results showed that manufacturers mainly focus on one-dimensional and attractive categories for product improvement process. Joung and Kim [14] used the KANO model to rank products from the customers’ perspective, and the results showed that the KANO model could recognize different types of customer preferences.Table 2
Summary of existing research to identify the product features for redesign based on focused areas.
Suggested models/methodsFocused areasCustomer satisfactionThe constraints of product improvementQi et al. [5]√×Wu et al. [16]√×Bi et al. [12]√×Hou et al. [8]√×Yang et al. [7]√×Zhou et al. [63]√×Jin et al. [64]√×Joung and Kim [14]√×Lee et al. [15]√×This study√√As presented, previous studies have discovered that the quality of online reviews is vital for both customers and product manufacturers. Many studies have investigated the key effective features of identifying helpful reviews. However, these studies did not explain how helpful reviews can benefit strategic decisions for the product improvement.Investigating the potential product features, the majority of researchers focused on mining customer preferences and opinions about products from customers’ perspectives by calculating the customer satisfaction level of the product features. However, many studies have neglected the effects ofproduct topics. Furthermore, the literature lacks a method for extracting and using customer preferences and opinions about products. Focusing on product redesign, manufacturers must not only consider the customers’ need but also the constraints of product redesign.Accordingly, this study proposes a novel method that aims to discover the product features from users’ implicit feedbacks for the product redesign. The research results have broad impacts on product redesign and product improvement strategies using the analytics of online reviews and capturing customer needs.


## 3. Methodology

Figure1 shows the framework of the proposed method (DPFR) for identifying to-be-improved product features based on online reviews for product redesign. In this framework, the process of identifying potential product features consists of three steps. The first step is to create a model to extract helpful reviews. Second, a structured model based on topic modeling is constructed to identify customers’ needs. Two major tasks in this step include the identification of product topics and the calculation of the importance of each topic. The candidate target features are stored in the structured model. Then, an index called feature attention (FA) is subsequently introduced from the structured model to measure the priority of all potential product features for the redesign. In the third step, a product feature selection model for the redesign is created, considering both the customer needs and the constraints of product redesign.Figure 1
The framework of the proposed DPFR model.Moreover, we considered three different constraints for our optimal configuration model. Product redesign cost and lead time refer to two very important factors that have been discussed in the previous research [67, 68]. Product redesign costs consist of traditional engineering costs such as materials, labor, and product transportation, as well as environmental protection costs throughout the lifecycle of products [67] and lead time refers to Hammami et al. [68] which cannot exceed the requested customer lead time. Further, low-carbon sustainability has become an important factor in product design [69, 70]. In the rapidly changing market, the lifecycle of products is shortened, which leads to rapid product iteration, contrary to the concept of low-carbon sustainable development [71–73]. Therefore, manufacturers should consider low-carbon sustainability when designing the products.


### 3.1. Identification of Helpful Reviews

Effectively identifying helpful reviews is important to product design. Review length, sentiment features, and lexical features are used to predict helpful reviews:(1)
Review Length (FL=fL1,fL2,). For each review, we selected the number of words (fL1) and the number of sentences (fL2) as the features.(2)
Sentiment Features (FST=fST1,fST2). For each review, we selected the number of positive words (fST1) and negative words (fST2) as sentiment features.(3)
Lexical Features (FSP=fSP1,fSP2,fSP3). For each review, we selected the number of nouns fSP1, the number of verbs (fSP2), the number of adverbs (fSP3), and the number of adjective (fSP4) as lexical features.Naive Bayes, KNN, RF, and SVM algorithms that have been widely used to identify helpful reviews were selected as the key algorithms for this study [17, 18, 39, 74].


### 3.2. The Structured Model

The structured model is constructed to describe the customers’ needs. In this phase, the latent product topics discussed by customers are identified based on helpful reviews that identify directions for product improvement. Hereafter, the keywords are extracted that reflect customers’ needs and preferences. Constructing the structured preference model consists of two phases. First, topic modeling was used to identify the latentproduct topics from helpful reviews and quantify the importance of each topic. Second, the FA index was introduced to calculate the priority and weight of all potential product features for the redesign.


#### 3.2.1. Topic Modeling

Before topic modeling, we need to process and filter the helpful reviews because the vocabularies also contain a large number of irrelevant words. The negation words, sentiment words, and degree words are filtered out. Then, LDA was selected as the topic model to train the helpful reviews. LDA can obtain the probability distribution of keywords on the topic. The keywords unrelated to the target products need to be manually removed.LDA is a three-layer Bayesian probability model, shown in Figure2. The model used in this study is briefly described as follows:(1)
LetM denotes the set of helpful reviews, K is the number of latent topics, Wp denotes the importance of each topic, and P∈1,K.(2)
Each helpful review,m (Nm is the length of m), has its topic distribution. The topic distribution follows a multinomial distribution. The parameters (θm) of the multinomial distribution follow the Dirichlet distribution, θm∼Dirichletα. α is the prior parameter of the Dirichlet distribution.(3)
Each topic (P∈1,K) has its word distribution, which follows the multinomial distribution. The parameters (∅k) of the multinomial distribution follow the Dirichlet distribution, ∅k∼Dirichletα. β is the prior parameter of the Dirichlet distribution.(4)
For each of the N wordswm,n, the first step is to choose a topic Zm,n, followed by choosing a word wm,n from pwm,n|Zm,n,β, a multinomial probability limits the topic Zm,n. This random process is repeated until all helpful reviews have been captured. Finally, we can obtain the topics-reviews matrix (TR Matrix), as shown in Figure 3. The importance of each topic (Wp) can be quantified from the topic mentioned in each review. WP is calculated as follows:(1)Cp=∑p=1K∑i=0nTRMartrixp,i,(2)Wp=Cp−CminCmax−Cmin,whereK is the number of topics, p∈1,K, and i denotes the review, i∈1,n.Figure 2
The graphical model of the LDA.Figure 3
Concepts for calculating the importance value of product topics.Although LDA can identify product topics, it cannot determine the number of product topics. The most popular model for selecting the number of topics is perplexity. Although perplexity reflects the predictive ability of the model, pursuing predictive ability blindly leads to the problem of having a large number of topics. Hence, only similar topics will appear, resulting in a low recognition of the topics [75, 76]. To solve this challenge, this study proposes a new measure (PSA) to select some topics. PSA is an extension of perplexity and considers the similarities between topics.PSA can be defined as follows:(3)Perplexity_varM=PerplexityMvarT.LetPerplexity_varM as the measurement index for selecting K. M is denoted as the set of helpful reviews.(4)PerplexityM=exp−∑m=1Mlogpwm∑m=1MNm.Letwm denote the word vector of m, m∈1,M. Nm is the number of words in m.(5)varT=∑T=1KcorreT−avg_cos2K,where varT represents the degree of deviation between each topic and the average similarity of the topics. K is the number of latent topics, and T∈1,K.(6)vg_cos=∑i=1K∑j=i+1KcorreZi,ZjK∗K−1/2,correZi,Zj=cosμ=∑i=1nAiBi∑i=1nAi2∑i=1nBi2=ABAB.Letavg_cos denote the average similarity of topics. A and B are n-dimensional topic vectors, A=A1,A2,…,An, B=B1,B2,…,Bn, cosμ is the cosine value of A and B.


#### 3.2.2. An Index for Product Redesign

1.Sentiment Analysis of Potential Features Associated with Each Topic. Customer sentiment contains positive, negative, and neutral emotions. Moreover, creative ideas and other needs are mainly hidden in positive and negative reviews. Hence, this study mainly focuses on positive and negative reviews.We define a 3-tuple,Fi, LPi, and GPi. In this 3-tuple, Fi denotes each product feature captured from helpful reviews. LPi denotes the local preference for the feature Fi. GPi denotes the global preference for the feature Fi.The local preference can be denoted as a setLPi=nij,pij|j=1,2,…,j, where nij, and pij are the negative and positive opinion frequencies on the feature i, in the helpful review j.(7)nij=countOpij−,(8)pij=countOpij+.The global preference can be denoted as a setGPiNi,Pi, where Ni and Pi are the negative and positive opinion frequencies, respectively, on the feature i in all helpful reviews.(9)Ni=∑j=1Mnij,(10)Pi=∑j=1Mpij.2.The Calculation of Feature Attention. Rai [77] defined a quantitative metric based on customers’ needs called the local global normalization measure (LGNM). LGNM uses three different types of term weighting: local, global, and normalization. In our study, we used LGNM to evaluate FA:(11)FAi=∑j=1MLij∗Gi∗Nj,where M is the total number of helpful reviews. Lij is denoted as local weight for feature i in the helpful review j. Gi is the global weight used to measure the importance of candidate feature i in all helpful reviews. Let Nj denotes the normalization factor to reduce the effect due to the lengths of the helpful reviews.The local weightLij is calculated by(12)Lij=log21+fij,where fij is the number of occurrences of the potential feature i in the helpful review j. It can be calculated as(13)fij=nij+pij.The local weightGi is calculated as(14)Gi=1+∑j=1Mfij/Filogfij/FilogM,where Fi is the number of occurrence of the feature i in all helpful reviews. It can be calculated as(15)Fi=Ni+Pj.The normalization factorNj is calculated by(16)Nj=1∑iGi∗Lij2.FA can be calculated by the following equation.(17)FAi=∑j=1Mlog21+nij+pij∗1+∑j=1Mnij+pij/Ni+Pjlognij+pij/Ni+PjlogM∗1∑iGi∗Lij2.


### 3.3. The Product Feature Selection Model for the Redesign

The potential features extracted from the structured model contain customer preferences to help designers understand the customers’ needs. However, the structured model only describes the opinions and attitudes of customers. During the product redesign process, the company’s possibilities and manufacturing limitations to implementing the customers’ needs must-be evaluated.In this study, we constructed an optimization model to select potential features to be improved in product redesign. The objective of this model is to satisfy the customers’ needs considering the production constraints. We considered the product redesign cost, redesign lead time, and carbon emissions as the primary constraints.Product redesign cost is the first constraint that needs to be considered. We considered traditional engineering costs, such as materials, labor, and product transportation, as well as environmental protection costs throughout the lifecycle of products [67].(18)RCi=TCi+ECi,where RCi is the cost of product redesign. TCi and ECi are denoted as the engineering costs and environmental protection costs for the potential feature i, respectively.In this study, we assumed that all redesign tasks were conducted at the same time. Thus, the redesign lead time was determined by the task requiring the longest lead time among all the redesign tasks.(19)RT=Maxti∗xi,where ti is the time of redesign task for potential feature i. xi is the 0–1 decision variable. If the potential feature i is selected, xi=1, otherwise xi=0.In this study, we set a carbon emission threshold for product redesign. We assumed that all product redesign tasks could not exceed this threshold.(20)∑1neixi≤E,where ei is denoted as the carbon emission of the redesign task for feature i, and E is the carbon emission threshold of all the redesign tasks.The objective function of the target product feature selection model in product redesign is defined in equation (21). As we considered the importance of product topics (wp), xpi is denoted as the decision variable when the candidate feature i in topic p is selected, xpi=1, otherwise, xpi=0. lp is denoted as the number of features in topic p.(21)maxU=∑p=1k∑i=1lpwp∗FApi∗xpi.The constraints of the target product feature selection model can be denoted as(22)∑p=1k∑1lpTCpi+ECpi∗xpi≤C,Maxtpi∗xpi≤T,∑p=1k∑1lpepi∗xpi≤E.


## 3.1. Identification of Helpful Reviews

Effectively identifying helpful reviews is important to product design. Review length, sentiment features, and lexical features are used to predict helpful reviews:(1)
Review Length (FL=fL1,fL2,). For each review, we selected the number of words (fL1) and the number of sentences (fL2) as the features.(2)
Sentiment Features (FST=fST1,fST2). For each review, we selected the number of positive words (fST1) and negative words (fST2) as sentiment features.(3)
Lexical Features (FSP=fSP1,fSP2,fSP3). For each review, we selected the number of nouns fSP1, the number of verbs (fSP2), the number of adverbs (fSP3), and the number of adjective (fSP4) as lexical features.Naive Bayes, KNN, RF, and SVM algorithms that have been widely used to identify helpful reviews were selected as the key algorithms for this study [17, 18, 39, 74].


## 3.2. The Structured Model

The structured model is constructed to describe the customers’ needs. In this phase, the latent product topics discussed by customers are identified based on helpful reviews that identify directions for product improvement. Hereafter, the keywords are extracted that reflect customers’ needs and preferences. Constructing the structured preference model consists of two phases. First, topic modeling was used to identify the latentproduct topics from helpful reviews and quantify the importance of each topic. Second, the FA index was introduced to calculate the priority and weight of all potential product features for the redesign.


### 3.2.1. Topic Modeling

Before topic modeling, we need to process and filter the helpful reviews because the vocabularies also contain a large number of irrelevant words. The negation words, sentiment words, and degree words are filtered out. Then, LDA was selected as the topic model to train the helpful reviews. LDA can obtain the probability distribution of keywords on the topic. The keywords unrelated to the target products need to be manually removed.LDA is a three-layer Bayesian probability model, shown in Figure2. The model used in this study is briefly described as follows:(1)
LetM denotes the set of helpful reviews, K is the number of latent topics, Wp denotes the importance of each topic, and P∈1,K.(2)
Each helpful review,m (Nm is the length of m), has its topic distribution. The topic distribution follows a multinomial distribution. The parameters (θm) of the multinomial distribution follow the Dirichlet distribution, θm∼Dirichletα. α is the prior parameter of the Dirichlet distribution.(3)
Each topic (P∈1,K) has its word distribution, which follows the multinomial distribution. The parameters (∅k) of the multinomial distribution follow the Dirichlet distribution, ∅k∼Dirichletα. β is the prior parameter of the Dirichlet distribution.(4)
For each of the N wordswm,n, the first step is to choose a topic Zm,n, followed by choosing a word wm,n from pwm,n|Zm,n,β, a multinomial probability limits the topic Zm,n. This random process is repeated until all helpful reviews have been captured. Finally, we can obtain the topics-reviews matrix (TR Matrix), as shown in Figure 3. The importance of each topic (Wp) can be quantified from the topic mentioned in each review. WP is calculated as follows:(1)Cp=∑p=1K∑i=0nTRMartrixp,i,(2)Wp=Cp−CminCmax−Cmin,whereK is the number of topics, p∈1,K, and i denotes the review, i∈1,n.Figure 2
The graphical model of the LDA.Figure 3
Concepts for calculating the importance value of product topics.Although LDA can identify product topics, it cannot determine the number of product topics. The most popular model for selecting the number of topics is perplexity. Although perplexity reflects the predictive ability of the model, pursuing predictive ability blindly leads to the problem of having a large number of topics. Hence, only similar topics will appear, resulting in a low recognition of the topics [75, 76]. To solve this challenge, this study proposes a new measure (PSA) to select some topics. PSA is an extension of perplexity and considers the similarities between topics.PSA can be defined as follows:(3)Perplexity_varM=PerplexityMvarT.LetPerplexity_varM as the measurement index for selecting K. M is denoted as the set of helpful reviews.(4)PerplexityM=exp−∑m=1Mlogpwm∑m=1MNm.Letwm denote the word vector of m, m∈1,M. Nm is the number of words in m.(5)varT=∑T=1KcorreT−avg_cos2K,where varT represents the degree of deviation between each topic and the average similarity of the topics. K is the number of latent topics, and T∈1,K.(6)vg_cos=∑i=1K∑j=i+1KcorreZi,ZjK∗K−1/2,correZi,Zj=cosμ=∑i=1nAiBi∑i=1nAi2∑i=1nBi2=ABAB.Letavg_cos denote the average similarity of topics. A and B are n-dimensional topic vectors, A=A1,A2,…,An, B=B1,B2,…,Bn, cosμ is the cosine value of A and B.


### 3.2.2. An Index for Product Redesign

1.Sentiment Analysis of Potential Features Associated with Each Topic. Customer sentiment contains positive, negative, and neutral emotions. Moreover, creative ideas and other needs are mainly hidden in positive and negative reviews. Hence, this study mainly focuses on positive and negative reviews.We define a 3-tuple,Fi, LPi, and GPi. In this 3-tuple, Fi denotes each product feature captured from helpful reviews. LPi denotes the local preference for the feature Fi. GPi denotes the global preference for the feature Fi.The local preference can be denoted as a setLPi=nij,pij|j=1,2,…,j, where nij, and pij are the negative and positive opinion frequencies on the feature i, in the helpful review j.(7)nij=countOpij−,(8)pij=countOpij+.The global preference can be denoted as a setGPiNi,Pi, where Ni and Pi are the negative and positive opinion frequencies, respectively, on the feature i in all helpful reviews.(9)Ni=∑j=1Mnij,(10)Pi=∑j=1Mpij.2.The Calculation of Feature Attention. Rai [77] defined a quantitative metric based on customers’ needs called the local global normalization measure (LGNM). LGNM uses three different types of term weighting: local, global, and normalization. In our study, we used LGNM to evaluate FA:(11)FAi=∑j=1MLij∗Gi∗Nj,where M is the total number of helpful reviews. Lij is denoted as local weight for feature i in the helpful review j. Gi is the global weight used to measure the importance of candidate feature i in all helpful reviews. Let Nj denotes the normalization factor to reduce the effect due to the lengths of the helpful reviews.The local weightLij is calculated by(12)Lij=log21+fij,where fij is the number of occurrences of the potential feature i in the helpful review j. It can be calculated as(13)fij=nij+pij.The local weightGi is calculated as(14)Gi=1+∑j=1Mfij/Filogfij/FilogM,where Fi is the number of occurrence of the feature i in all helpful reviews. It can be calculated as(15)Fi=Ni+Pj.The normalization factorNj is calculated by(16)Nj=1∑iGi∗Lij2.FA can be calculated by the following equation.(17)FAi=∑j=1Mlog21+nij+pij∗1+∑j=1Mnij+pij/Ni+Pjlognij+pij/Ni+PjlogM∗1∑iGi∗Lij2.


## 3.2.1. Topic Modeling

Before topic modeling, we need to process and filter the helpful reviews because the vocabularies also contain a large number of irrelevant words. The negation words, sentiment words, and degree words are filtered out. Then, LDA was selected as the topic model to train the helpful reviews. LDA can obtain the probability distribution of keywords on the topic. The keywords unrelated to the target products need to be manually removed.LDA is a three-layer Bayesian probability model, shown in Figure2. The model used in this study is briefly described as follows:(1)
LetM denotes the set of helpful reviews, K is the number of latent topics, Wp denotes the importance of each topic, and P∈1,K.(2)
Each helpful review,m (Nm is the length of m), has its topic distribution. The topic distribution follows a multinomial distribution. The parameters (θm) of the multinomial distribution follow the Dirichlet distribution, θm∼Dirichletα. α is the prior parameter of the Dirichlet distribution.(3)
Each topic (P∈1,K) has its word distribution, which follows the multinomial distribution. The parameters (∅k) of the multinomial distribution follow the Dirichlet distribution, ∅k∼Dirichletα. β is the prior parameter of the Dirichlet distribution.(4)
For each of the N wordswm,n, the first step is to choose a topic Zm,n, followed by choosing a word wm,n from pwm,n|Zm,n,β, a multinomial probability limits the topic Zm,n. This random process is repeated until all helpful reviews have been captured. Finally, we can obtain the topics-reviews matrix (TR Matrix), as shown in Figure 3. The importance of each topic (Wp) can be quantified from the topic mentioned in each review. WP is calculated as follows:(1)Cp=∑p=1K∑i=0nTRMartrixp,i,(2)Wp=Cp−CminCmax−Cmin,whereK is the number of topics, p∈1,K, and i denotes the review, i∈1,n.Figure 2
The graphical model of the LDA.Figure 3
Concepts for calculating the importance value of product topics.Although LDA can identify product topics, it cannot determine the number of product topics. The most popular model for selecting the number of topics is perplexity. Although perplexity reflects the predictive ability of the model, pursuing predictive ability blindly leads to the problem of having a large number of topics. Hence, only similar topics will appear, resulting in a low recognition of the topics [75, 76]. To solve this challenge, this study proposes a new measure (PSA) to select some topics. PSA is an extension of perplexity and considers the similarities between topics.PSA can be defined as follows:(3)Perplexity_varM=PerplexityMvarT.LetPerplexity_varM as the measurement index for selecting K. M is denoted as the set of helpful reviews.(4)PerplexityM=exp−∑m=1Mlogpwm∑m=1MNm.Letwm denote the word vector of m, m∈1,M. Nm is the number of words in m.(5)varT=∑T=1KcorreT−avg_cos2K,where varT represents the degree of deviation between each topic and the average similarity of the topics. K is the number of latent topics, and T∈1,K.(6)vg_cos=∑i=1K∑j=i+1KcorreZi,ZjK∗K−1/2,correZi,Zj=cosμ=∑i=1nAiBi∑i=1nAi2∑i=1nBi2=ABAB.Letavg_cos denote the average similarity of topics. A and B are n-dimensional topic vectors, A=A1,A2,…,An, B=B1,B2,…,Bn, cosμ is the cosine value of A and B.


## 3.2.2. An Index for Product Redesign

1.Sentiment Analysis of Potential Features Associated with Each Topic. Customer sentiment contains positive, negative, and neutral emotions. Moreover, creative ideas and other needs are mainly hidden in positive and negative reviews. Hence, this study mainly focuses on positive and negative reviews.We define a 3-tuple,Fi, LPi, and GPi. In this 3-tuple, Fi denotes each product feature captured from helpful reviews. LPi denotes the local preference for the feature Fi. GPi denotes the global preference for the feature Fi.The local preference can be denoted as a setLPi=nij,pij|j=1,2,…,j, where nij, and pij are the negative and positive opinion frequencies on the feature i, in the helpful review j.(7)nij=countOpij−,(8)pij=countOpij+.The global preference can be denoted as a setGPiNi,Pi, where Ni and Pi are the negative and positive opinion frequencies, respectively, on the feature i in all helpful reviews.(9)Ni=∑j=1Mnij,(10)Pi=∑j=1Mpij.2.The Calculation of Feature Attention. Rai [77] defined a quantitative metric based on customers’ needs called the local global normalization measure (LGNM). LGNM uses three different types of term weighting: local, global, and normalization. In our study, we used LGNM to evaluate FA:(11)FAi=∑j=1MLij∗Gi∗Nj,where M is the total number of helpful reviews. Lij is denoted as local weight for feature i in the helpful review j. Gi is the global weight used to measure the importance of candidate feature i in all helpful reviews. Let Nj denotes the normalization factor to reduce the effect due to the lengths of the helpful reviews.The local weightLij is calculated by(12)Lij=log21+fij,where fij is the number of occurrences of the potential feature i in the helpful review j. It can be calculated as(13)fij=nij+pij.The local weightGi is calculated as(14)Gi=1+∑j=1Mfij/Filogfij/FilogM,where Fi is the number of occurrence of the feature i in all helpful reviews. It can be calculated as(15)Fi=Ni+Pj.The normalization factorNj is calculated by(16)Nj=1∑iGi∗Lij2.FA can be calculated by the following equation.(17)FAi=∑j=1Mlog21+nij+pij∗1+∑j=1Mnij+pij/Ni+Pjlognij+pij/Ni+PjlogM∗1∑iGi∗Lij2.


## 3.3. The Product Feature Selection Model for the Redesign

The potential features extracted from the structured model contain customer preferences to help designers understand the customers’ needs. However, the structured model only describes the opinions and attitudes of customers. During the product redesign process, the company’s possibilities and manufacturing limitations to implementing the customers’ needs must-be evaluated.In this study, we constructed an optimization model to select potential features to be improved in product redesign. The objective of this model is to satisfy the customers’ needs considering the production constraints. We considered the product redesign cost, redesign lead time, and carbon emissions as the primary constraints.Product redesign cost is the first constraint that needs to be considered. We considered traditional engineering costs, such as materials, labor, and product transportation, as well as environmental protection costs throughout the lifecycle of products [67].(18)RCi=TCi+ECi,where RCi is the cost of product redesign. TCi and ECi are denoted as the engineering costs and environmental protection costs for the potential feature i, respectively.In this study, we assumed that all redesign tasks were conducted at the same time. Thus, the redesign lead time was determined by the task requiring the longest lead time among all the redesign tasks.(19)RT=Maxti∗xi,where ti is the time of redesign task for potential feature i. xi is the 0–1 decision variable. If the potential feature i is selected, xi=1, otherwise xi=0.In this study, we set a carbon emission threshold for product redesign. We assumed that all product redesign tasks could not exceed this threshold.(20)∑1neixi≤E,where ei is denoted as the carbon emission of the redesign task for feature i, and E is the carbon emission threshold of all the redesign tasks.The objective function of the target product feature selection model in product redesign is defined in equation (21). As we considered the importance of product topics (wp), xpi is denoted as the decision variable when the candidate feature i in topic p is selected, xpi=1, otherwise, xpi=0. lp is denoted as the number of features in topic p.(21)maxU=∑p=1k∑i=1lpwp∗FApi∗xpi.The constraints of the target product feature selection model can be denoted as(22)∑p=1k∑1lpTCpi+ECpi∗xpi≤C,Maxtpi∗xpi≤T,∑p=1k∑1lpepi∗xpi≤E.


## 4. Evaluation

### 4.1. Data

In recent years, with the rapid development of social media platforms, new media, mainly in the form of short videos, have become popular. These short-video platforms provide rich real-time data and are popular channels for customers to exchange their opinions about the product. Bilibili is one of the largest short-video platforms in China, and there are many online reviews of products provided by customers on the platform. We selected Matepad 11, a tablet computer manufactured by Huawei, as the product to test our model due to the availability of a large number of online reviews about Matepad 11 on Bilibili. Based on the comprehensive ranking of uploaded videos by Bilibili, 10 videos about Matepad 11 were selected to collect online reviews.Python 3.7 was used to clean the online reviews. To clean the data, we mainly removed duplicated and abnormal data, normalized the text, segmented the words, and deleted stop words. This resulted in a collection of 21112 customer reviews about Huawei Matepad 11.


### 4.2. Identification of Helpful Reviews

A model to identify helpful reviews was created based on the explanations in Section3.1. We divided the training and test datasets according to a ratio of 7:3. The average 10-fold cross-validation was used as the performance of each model. In our experiments, we have set enough epoch for model training, and the parameters of each were iteratively optimized until the model converged on our training set. The performance of each model on the testing set is shown in Table 3. Precision, recall, and the F-measure were used to evaluate the performance of the classification algorithms:(23)precision=P=TPTP+FP,recall=R=TPTP+FN,F_measure=2∗P∗RP+R,where F_measure denotes the harmonic mean of precision and recall. True positive (TP) refers to positive samples that are correctly judged as positive, false positive (FP) refers to negative samples that are incorrectly judged as positive, and false negative (FN) refers to positive samples that are incorrectly judged as negative. Table 3 shows the results obtained using different models of helpful review identification.Table 3
Results of identifying helpful reviews.
ModelsPrecisionRecallF-measureNB0.8570.8620.859KNN0.8230.8360.829RF0.8450.8350.840SVM0.8730.8650.868Based on the numbers in Table3, the SVM as the kernel algorithm performed better than NB, KNN, and RF algorithms in reviews identification. The precision of the model reached its best when SVM was used. The performance of SVM and NB on recall was close, but the F-measure was best when SVM was used. Therefore, we chose SVM to identify helpful reviews, resulting in the identification of 4111 helpful reviews.


### 4.3. Construction of the Structured Model

#### 4.3.1. Topic Modeling

As shown in Figure4(a), the optimal number of topics selected by the PSV was 4. The optimal number of topics selected by perplexity was 6. The distances between each latent topic obtained using PSV and perplexity are shown in Figures 5(a) and 5(b), respectively. As shown in Figure 5(b), three topics with high similarity were obtained using perplexity. Therefore, PSV was selected to calculate the number of topics.Figure 4
The selected optimal number of topics. (a) Select the optimal number of topics by PSV. (b) Select the optimal number of topics by perplexity.
(a)(b)Figure 5
Distance between latent topics. (a) Topic distance calculated by the PSV. (b) Topic distance calculated by perplexity.
(a)(b)We named four topics selected from thePSV, as shown in Table 4. The real name of each topic was manually defined by its major features. The features such as “notebook” or “design’ in “Topic 1” relate to office scenes. Thus, the label of “Topic 1” can be described as “Office Scene.” Similarly, the label of the other topics can be described separately as: “Operating System,” “Audio-Visual entertainment,” “Relevant Accessory and Hardware.” The degree of importance for each topic was calculated using equations (1) and (2),WP=W1,W2,W3,W4=0.24,0.35,0.16,0.25.Table 4
Results of topic modeling.
TopicsFeaturesTopic 1, office scenesNotebook, write, multi-screen collaboration, Huawei share, cloud, design, document, cast screen, MeeTimeTopic 2, operating systemSoftware usage ecology, bring two into view, app snippets, screen refresh rate, supper device, control panel, software optimization, backend system, search, system fluencyTopic 3, audio-visual entertainmentFilm, painting, wallpapers, music, download, advertisement, network, photos, gameTopic 4, relevant accessory and hardwareScreen, camera, keyboard, color, speaker, memory, CPU, fingerprint, battery, stylus, bluetooth


#### 4.3.2. The Calculation ofFA

A set of 38 features from the four topics were extracted, as shown in Table4. The local and global preferences were calculated using equations (7)–(16). The FA of each feature was calculated using equation (17). The results are shown in Table 5.Table 5
Results of the structured model.
TopicpCandidate featuresiLocal preferenceLPiGlobal preferenceGPiFeature attentionFAinijj=1,2,…,4111pijj=1,2,…,4111NijPijTopic1Notebookn1,1=1p1,1=0187151156.52n1,2=0……p1,12=1n1,4110=0…n1,4111=0p1,4111=0...Mee timen1,1=0p1,1=01318683.72……n1,14=1p1,22=1……n1,4111=0p1,4111=0Topic2Software usage ecologyn1,1=1p1,1=021789137.47n1,2=0……p1,17=1n1,4110=0…n1,4111=0p1,4111=0…System fluencyn1,1=0p1,1=0164155134.64……n1,21=2p1,16=1……n1,4111=0p1,4111=0Topic3Filmn1,1=0p1,1=04612562.88……n1,89=0p1,12=45……n1,4111=0p1,4111=0…Gamen1,1=0p1,1=0564740.51……n1,121=1p1,78=1……n1,4111=0p1,4111=0Topic4Screenn1,1=0p1,1=05417980.79……n1,48=2p1,14=1……n1,4111=0p1,4111=0…Bluetoothn1,1=0p1,1=0348756.06n1,2=1……p1,41=1n1,4110=0…n1,4111=0p1,4111=0


### 4.4. Identifying the Potential Product Features for Product Redesign

In this empirical study, the total budget for product redesign was calculated as 3000000 dollars, the longest lead time for product redesign was calculated as 2400 hours, and carbon emission was calculated as 10000 kg. The to-be-improved product feature selection model was defined by(24)maxU=∑p=14∑i=1lpwp∗FApi∗xpi.Subject to(25)∑p=14∑i=1lpTCpi+ECpi∗xpi≤3000000Maxtpi∗xpi≤2400,∑p=14∑i=1lpepi∗xpi≤10000.As shown in Figure6, after solving the target feature selection model for product redesign, the top 10 features were identified from the potential features. The manufacturers can choose the number of features for redesign according to their actual situation of product redesign. In this study, the top 10 features were selected as the target features for redesign. These 10 features are software usage ecology, system fluency, CPU, multi-screen collaboration, supper device, bring two into view, notebook, Huawei share, screen refresh rate, and document. According to the results, the features software usage ecology and system fluency have significant potential for the product redesign. The features multi-screen collaboration, notebook, Huawei share, and document belong to Topic 1. The features software usage ecology, system fluency, upper device, bring two into view, and screen refresh rate belong to Topic 2. The feature CPU belongs to Topic 4. Based on the knowledge in Section 4.2, Topics 1, 2, 3, and 4 are denoted as “Office Scenes, Operating System, Audio-Visual Entertainment, and Related Accessories and Hardware. As shown in Figure 7, the number of target features accounted for 50% in Topic 2, 40% in Topic 1, and 10% in Topic 4. Based on the results from Figure 8, the topic “Operating System” had the largest proportion, and no feature was selected in Topic 3 as the potential features for the product redesign. However, this might be related to the strategic positioning of the Huawei Matepad series because the main selling points of the Huawei Matepad series are the operating system and smart office.Figure 6
The target product features for the product redesign.Figure 7
Proportion of the target product features for redesign.Figure 8
Line chart of percentages for each topic in different transformation factors. (a) ℎ = 0.05. (b) ℎ = 0.1. (c) ℎ = 0.15.
(a)(b)(c)


### 4.5. The Topic Weight Analysis for the Product Redesign

This study changed the weight of the topic importance to better describe the impact of the topic importance (w1,w2,w3,w4) on the selection of product design attributes. We set a transformation factor (h), which represents the increase or decrease of the weight of the topic importance each time, and h was set as 0.05, 0.1, and 0.15, respectively. After setting h, six experiments were conducted. For example, when h=0.05, in Experiment 1, w1 and w2 remained unchanged, w3 increased by 0.05, and w4 decreased by 0.05. Similar to Experiment 1, other experiments also had two control variables, with the weights of the other two topics directionally changed.Figure8 shows that with the change in topic weight, the percentage of each topic was constantly changing. Hence, the experiments illustrate that the changes in w1， w2， w3, and w4 had a significant impact on the selection of product redesign features. With the increasing of transformation factor, the magnitude of changing percentage of each topic was increasing, as shown in Figure 9. Moreover, as shown in Figure 10, compared to w1, w2 and w4,w3 was relatively weak, and w2 had the greatest influence. In this study, the feature selection model was effective in terms of topic importance. Topic importance is an indispensable key factor in product redesign. Customers and manufacturers paid significant attention to Topic 1—Office Scene, Topic 2—Operation System, and Topic 4—Relevant Accessory and Hardware, whereas Topic 3—Audio-visual Entertainment was of less concern. Among the four topics, Topic 2 was the most important future product redesign direction. Although the change in topic weight affected its proportion, Topic 2 accounted for a large proportion in each experiment.Figure 9
Line chart of changing trend of each topic in different transformation factors. (a) The changing trend of topic 1 in different transformation factors. (b) The changing trend of topic 2 in different transformation factors. (c) The changing trend of topic 3 in different transformation factors. (d) The changing trend of topic 4 in different transformation factors.
(a)(b)(c)(d)Figure 10
Radar map of percentages for each topic in different transformation factors. (a)h=0.05. (b) h=0.1. (c) h=0.15.
(a)(b)(c)


### 4.6. Comparison with Other Models

To verify the validity of the proposed model (DPFR), we compared it with other models. Bi et al. [12] proposed a method from the customers’ needs perspective. In their method, LDA is used to extract different customer satisfaction dimensions, and then the sentiment orientations of the customer satisfaction dimensions are measured based on the sentiment analysis. Therefore, the category of each customer satisfaction dimension is identified by the KANO model. According to Bi et al. [12], for product improvement process, manufacturers mainly focus on one-dimensional and attractive categories, and the priority order of one-dimensional is higher than the attractive category. Therefore, product features that belong to one-dimensional and attractive categories are selected for improvement. By contrast, in the DPFR model, the customers’ needs and production constraints are both considered. To further verify the validity of the DPFR, we need to have equal functionalities across the different models being compared. Hence, the production constraints proposed in Section 3.3 are removed from the DPFR model, and the selection model is constructed as equation (22). The results of the three methods are shown in Table 6.(26)maxU=∑p=1k∑i=1lpwp∗FApi∗xpi.Table 6
Results of the three methods.
MethodsTarget featuresBi et al. [12]Software usage ecology, speaker, bring two into view, supper device, fingerprintSystem fluency, system fluency, battery, color, write, camera, stylus, notebook, keyboard, screenDPFRSoftware usage ecology, system fluency, CPU, multi-screen collaboration, supper device, bring two into view, notebook, huawei share, screen refresh rate, documentDPFR without considering the production constraintsSoftware usage ecology, system fluency, CPU, multi-screen collaboration, supper device, bring two into view, notebook, camera, stylus, keyboardAs shown in Table6, ignoring the product manufacturing constraints, 80% of the target features extracted by DPFR are the same as the features extracted using the KANO model by Bi et al. [12]. Hence, we can validate the effectiveness of DPFR model when focusing only on customers’ needs. Including the constraints of product redesign, the results of DPFR highlight 3 target features different from the DPFR without considering the product constraints. The 3 target features are “huaweiwei share, screen refresh rate, and document” when manufacturing constraints are included in DPFR. While the 3 target features are “camera, stylus, and keyboard” when using DPFR without considering the product constraints. The reason for the mentioned difference is that redesign cost, redesign time, and carbon emissions are considered as constraints for product redesign in the DPFR. In the model of DPFR excluding product constraints, “camera, stylus, and keyboard” are highlighted, indicating that they attract attention from the customers, while these features are demanding regarding cost, time, and carbon emissions. In the actual production process, in order to select product features for the effective redesign, manufacturers should consider not only the preference of consumers but also the product constraints for the redesign.


## 4.1. Data

In recent years, with the rapid development of social media platforms, new media, mainly in the form of short videos, have become popular. These short-video platforms provide rich real-time data and are popular channels for customers to exchange their opinions about the product. Bilibili is one of the largest short-video platforms in China, and there are many online reviews of products provided by customers on the platform. We selected Matepad 11, a tablet computer manufactured by Huawei, as the product to test our model due to the availability of a large number of online reviews about Matepad 11 on Bilibili. Based on the comprehensive ranking of uploaded videos by Bilibili, 10 videos about Matepad 11 were selected to collect online reviews.Python 3.7 was used to clean the online reviews. To clean the data, we mainly removed duplicated and abnormal data, normalized the text, segmented the words, and deleted stop words. This resulted in a collection of 21112 customer reviews about Huawei Matepad 11.


## 4.2. Identification of Helpful Reviews

A model to identify helpful reviews was created based on the explanations in Section3.1. We divided the training and test datasets according to a ratio of 7:3. The average 10-fold cross-validation was used as the performance of each model. In our experiments, we have set enough epoch for model training, and the parameters of each were iteratively optimized until the model converged on our training set. The performance of each model on the testing set is shown in Table 3. Precision, recall, and the F-measure were used to evaluate the performance of the classification algorithms:(23)precision=P=TPTP+FP,recall=R=TPTP+FN,F_measure=2∗P∗RP+R,where F_measure denotes the harmonic mean of precision and recall. True positive (TP) refers to positive samples that are correctly judged as positive, false positive (FP) refers to negative samples that are incorrectly judged as positive, and false negative (FN) refers to positive samples that are incorrectly judged as negative. Table 3 shows the results obtained using different models of helpful review identification.Table 3
Results of identifying helpful reviews.
ModelsPrecisionRecallF-measureNB0.8570.8620.859KNN0.8230.8360.829RF0.8450.8350.840SVM0.8730.8650.868Based on the numbers in Table3, the SVM as the kernel algorithm performed better than NB, KNN, and RF algorithms in reviews identification. The precision of the model reached its best when SVM was used. The performance of SVM and NB on recall was close, but the F-measure was best when SVM was used. Therefore, we chose SVM to identify helpful reviews, resulting in the identification of 4111 helpful reviews.


## 4.3. Construction of the Structured Model

### 4.3.1. Topic Modeling

As shown in Figure4(a), the optimal number of topics selected by the PSV was 4. The optimal number of topics selected by perplexity was 6. The distances between each latent topic obtained using PSV and perplexity are shown in Figures 5(a) and 5(b), respectively. As shown in Figure 5(b), three topics with high similarity were obtained using perplexity. Therefore, PSV was selected to calculate the number of topics.Figure 4
The selected optimal number of topics. (a) Select the optimal number of topics by PSV. (b) Select the optimal number of topics by perplexity.
(a)(b)Figure 5
Distance between latent topics. (a) Topic distance calculated by the PSV. (b) Topic distance calculated by perplexity.
(a)(b)We named four topics selected from thePSV, as shown in Table 4. The real name of each topic was manually defined by its major features. The features such as “notebook” or “design’ in “Topic 1” relate to office scenes. Thus, the label of “Topic 1” can be described as “Office Scene.” Similarly, the label of the other topics can be described separately as: “Operating System,” “Audio-Visual entertainment,” “Relevant Accessory and Hardware.” The degree of importance for each topic was calculated using equations (1) and (2),WP=W1,W2,W3,W4=0.24,0.35,0.16,0.25.Table 4
Results of topic modeling.
TopicsFeaturesTopic 1, office scenesNotebook, write, multi-screen collaboration, Huawei share, cloud, design, document, cast screen, MeeTimeTopic 2, operating systemSoftware usage ecology, bring two into view, app snippets, screen refresh rate, supper device, control panel, software optimization, backend system, search, system fluencyTopic 3, audio-visual entertainmentFilm, painting, wallpapers, music, download, advertisement, network, photos, gameTopic 4, relevant accessory and hardwareScreen, camera, keyboard, color, speaker, memory, CPU, fingerprint, battery, stylus, bluetooth


### 4.3.2. The Calculation ofFA

A set of 38 features from the four topics were extracted, as shown in Table4. The local and global preferences were calculated using equations (7)–(16). The FA of each feature was calculated using equation (17). The results are shown in Table 5.Table 5
Results of the structured model.
TopicpCandidate featuresiLocal preferenceLPiGlobal preferenceGPiFeature attentionFAinijj=1,2,…,4111pijj=1,2,…,4111NijPijTopic1Notebookn1,1=1p1,1=0187151156.52n1,2=0……p1,12=1n1,4110=0…n1,4111=0p1,4111=0...Mee timen1,1=0p1,1=01318683.72……n1,14=1p1,22=1……n1,4111=0p1,4111=0Topic2Software usage ecologyn1,1=1p1,1=021789137.47n1,2=0……p1,17=1n1,4110=0…n1,4111=0p1,4111=0…System fluencyn1,1=0p1,1=0164155134.64……n1,21=2p1,16=1……n1,4111=0p1,4111=0Topic3Filmn1,1=0p1,1=04612562.88……n1,89=0p1,12=45……n1,4111=0p1,4111=0…Gamen1,1=0p1,1=0564740.51……n1,121=1p1,78=1……n1,4111=0p1,4111=0Topic4Screenn1,1=0p1,1=05417980.79……n1,48=2p1,14=1……n1,4111=0p1,4111=0…Bluetoothn1,1=0p1,1=0348756.06n1,2=1……p1,41=1n1,4110=0…n1,4111=0p1,4111=0


## 4.3.1. Topic Modeling

As shown in Figure4(a), the optimal number of topics selected by the PSV was 4. The optimal number of topics selected by perplexity was 6. The distances between each latent topic obtained using PSV and perplexity are shown in Figures 5(a) and 5(b), respectively. As shown in Figure 5(b), three topics with high similarity were obtained using perplexity. Therefore, PSV was selected to calculate the number of topics.Figure 4
The selected optimal number of topics. (a) Select the optimal number of topics by PSV. (b) Select the optimal number of topics by perplexity.
(a)(b)Figure 5
Distance between latent topics. (a) Topic distance calculated by the PSV. (b) Topic distance calculated by perplexity.
(a)(b)We named four topics selected from thePSV, as shown in Table 4. The real name of each topic was manually defined by its major features. The features such as “notebook” or “design’ in “Topic 1” relate to office scenes. Thus, the label of “Topic 1” can be described as “Office Scene.” Similarly, the label of the other topics can be described separately as: “Operating System,” “Audio-Visual entertainment,” “Relevant Accessory and Hardware.” The degree of importance for each topic was calculated using equations (1) and (2),WP=W1,W2,W3,W4=0.24,0.35,0.16,0.25.Table 4
Results of topic modeling.
TopicsFeaturesTopic 1, office scenesNotebook, write, multi-screen collaboration, Huawei share, cloud, design, document, cast screen, MeeTimeTopic 2, operating systemSoftware usage ecology, bring two into view, app snippets, screen refresh rate, supper device, control panel, software optimization, backend system, search, system fluencyTopic 3, audio-visual entertainmentFilm, painting, wallpapers, music, download, advertisement, network, photos, gameTopic 4, relevant accessory and hardwareScreen, camera, keyboard, color, speaker, memory, CPU, fingerprint, battery, stylus, bluetooth


## 4.3.2. The Calculation ofFA

A set of 38 features from the four topics were extracted, as shown in Table4. The local and global preferences were calculated using equations (7)–(16). The FA of each feature was calculated using equation (17). The results are shown in Table 5.Table 5
Results of the structured model.
TopicpCandidate featuresiLocal preferenceLPiGlobal preferenceGPiFeature attentionFAinijj=1,2,…,4111pijj=1,2,…,4111NijPijTopic1Notebookn1,1=1p1,1=0187151156.52n1,2=0……p1,12=1n1,4110=0…n1,4111=0p1,4111=0...Mee timen1,1=0p1,1=01318683.72……n1,14=1p1,22=1……n1,4111=0p1,4111=0Topic2Software usage ecologyn1,1=1p1,1=021789137.47n1,2=0……p1,17=1n1,4110=0…n1,4111=0p1,4111=0…System fluencyn1,1=0p1,1=0164155134.64……n1,21=2p1,16=1……n1,4111=0p1,4111=0Topic3Filmn1,1=0p1,1=04612562.88……n1,89=0p1,12=45……n1,4111=0p1,4111=0…Gamen1,1=0p1,1=0564740.51……n1,121=1p1,78=1……n1,4111=0p1,4111=0Topic4Screenn1,1=0p1,1=05417980.79……n1,48=2p1,14=1……n1,4111=0p1,4111=0…Bluetoothn1,1=0p1,1=0348756.06n1,2=1……p1,41=1n1,4110=0…n1,4111=0p1,4111=0


## 4.4. Identifying the Potential Product Features for Product Redesign

In this empirical study, the total budget for product redesign was calculated as 3000000 dollars, the longest lead time for product redesign was calculated as 2400 hours, and carbon emission was calculated as 10000 kg. The to-be-improved product feature selection model was defined by(24)maxU=∑p=14∑i=1lpwp∗FApi∗xpi.Subject to(25)∑p=14∑i=1lpTCpi+ECpi∗xpi≤3000000Maxtpi∗xpi≤2400,∑p=14∑i=1lpepi∗xpi≤10000.As shown in Figure6, after solving the target feature selection model for product redesign, the top 10 features were identified from the potential features. The manufacturers can choose the number of features for redesign according to their actual situation of product redesign. In this study, the top 10 features were selected as the target features for redesign. These 10 features are software usage ecology, system fluency, CPU, multi-screen collaboration, supper device, bring two into view, notebook, Huawei share, screen refresh rate, and document. According to the results, the features software usage ecology and system fluency have significant potential for the product redesign. The features multi-screen collaboration, notebook, Huawei share, and document belong to Topic 1. The features software usage ecology, system fluency, upper device, bring two into view, and screen refresh rate belong to Topic 2. The feature CPU belongs to Topic 4. Based on the knowledge in Section 4.2, Topics 1, 2, 3, and 4 are denoted as “Office Scenes, Operating System, Audio-Visual Entertainment, and Related Accessories and Hardware. As shown in Figure 7, the number of target features accounted for 50% in Topic 2, 40% in Topic 1, and 10% in Topic 4. Based on the results from Figure 8, the topic “Operating System” had the largest proportion, and no feature was selected in Topic 3 as the potential features for the product redesign. However, this might be related to the strategic positioning of the Huawei Matepad series because the main selling points of the Huawei Matepad series are the operating system and smart office.Figure 6
The target product features for the product redesign.Figure 7
Proportion of the target product features for redesign.Figure 8
Line chart of percentages for each topic in different transformation factors. (a) ℎ = 0.05. (b) ℎ = 0.1. (c) ℎ = 0.15.
(a)(b)(c)


## 4.5. The Topic Weight Analysis for the Product Redesign

This study changed the weight of the topic importance to better describe the impact of the topic importance (w1,w2,w3,w4) on the selection of product design attributes. We set a transformation factor (h), which represents the increase or decrease of the weight of the topic importance each time, and h was set as 0.05, 0.1, and 0.15, respectively. After setting h, six experiments were conducted. For example, when h=0.05, in Experiment 1, w1 and w2 remained unchanged, w3 increased by 0.05, and w4 decreased by 0.05. Similar to Experiment 1, other experiments also had two control variables, with the weights of the other two topics directionally changed.Figure8 shows that with the change in topic weight, the percentage of each topic was constantly changing. Hence, the experiments illustrate that the changes in w1， w2， w3, and w4 had a significant impact on the selection of product redesign features. With the increasing of transformation factor, the magnitude of changing percentage of each topic was increasing, as shown in Figure 9. Moreover, as shown in Figure 10, compared to w1, w2 and w4,w3 was relatively weak, and w2 had the greatest influence. In this study, the feature selection model was effective in terms of topic importance. Topic importance is an indispensable key factor in product redesign. Customers and manufacturers paid significant attention to Topic 1—Office Scene, Topic 2—Operation System, and Topic 4—Relevant Accessory and Hardware, whereas Topic 3—Audio-visual Entertainment was of less concern. Among the four topics, Topic 2 was the most important future product redesign direction. Although the change in topic weight affected its proportion, Topic 2 accounted for a large proportion in each experiment.Figure 9
Line chart of changing trend of each topic in different transformation factors. (a) The changing trend of topic 1 in different transformation factors. (b) The changing trend of topic 2 in different transformation factors. (c) The changing trend of topic 3 in different transformation factors. (d) The changing trend of topic 4 in different transformation factors.
(a)(b)(c)(d)Figure 10
Radar map of percentages for each topic in different transformation factors. (a)h=0.05. (b) h=0.1. (c) h=0.15.
(a)(b)(c)


## 4.6. Comparison with Other Models

To verify the validity of the proposed model (DPFR), we compared it with other models. Bi et al. [12] proposed a method from the customers’ needs perspective. In their method, LDA is used to extract different customer satisfaction dimensions, and then the sentiment orientations of the customer satisfaction dimensions are measured based on the sentiment analysis. Therefore, the category of each customer satisfaction dimension is identified by the KANO model. According to Bi et al. [12], for product improvement process, manufacturers mainly focus on one-dimensional and attractive categories, and the priority order of one-dimensional is higher than the attractive category. Therefore, product features that belong to one-dimensional and attractive categories are selected for improvement. By contrast, in the DPFR model, the customers’ needs and production constraints are both considered. To further verify the validity of the DPFR, we need to have equal functionalities across the different models being compared. Hence, the production constraints proposed in Section 3.3 are removed from the DPFR model, and the selection model is constructed as equation (22). The results of the three methods are shown in Table 6.(26)maxU=∑p=1k∑i=1lpwp∗FApi∗xpi.Table 6
Results of the three methods.
MethodsTarget featuresBi et al. [12]Software usage ecology, speaker, bring two into view, supper device, fingerprintSystem fluency, system fluency, battery, color, write, camera, stylus, notebook, keyboard, screenDPFRSoftware usage ecology, system fluency, CPU, multi-screen collaboration, supper device, bring two into view, notebook, huawei share, screen refresh rate, documentDPFR without considering the production constraintsSoftware usage ecology, system fluency, CPU, multi-screen collaboration, supper device, bring two into view, notebook, camera, stylus, keyboardAs shown in Table6, ignoring the product manufacturing constraints, 80% of the target features extracted by DPFR are the same as the features extracted using the KANO model by Bi et al. [12]. Hence, we can validate the effectiveness of DPFR model when focusing only on customers’ needs. Including the constraints of product redesign, the results of DPFR highlight 3 target features different from the DPFR without considering the product constraints. The 3 target features are “huaweiwei share, screen refresh rate, and document” when manufacturing constraints are included in DPFR. While the 3 target features are “camera, stylus, and keyboard” when using DPFR without considering the product constraints. The reason for the mentioned difference is that redesign cost, redesign time, and carbon emissions are considered as constraints for product redesign in the DPFR. In the model of DPFR excluding product constraints, “camera, stylus, and keyboard” are highlighted, indicating that they attract attention from the customers, while these features are demanding regarding cost, time, and carbon emissions. In the actual production process, in order to select product features for the effective redesign, manufacturers should consider not only the preference of consumers but also the product constraints for the redesign.


## 5. Discussion and Conclusions

In this study, we propose a novel model (DPFR) to identify the potential features for product redesign from both the customers’ and the manufacturers’ perspectives. First, we used semantic analysis to build feature engineering and to create machine learning models to identify helpful reviews. The results showed that many useless reviews were removed indicating that identifying useful reviews is an important step, which can reduce the redundancy of useless reviews and improve the computational efficiency to discover product features for the redesign. Second, a structured model was constructed based on helpful reviews. In this step, LDA was used to identify latentproduct topics that customers discussed in helpful reviews and to quantify the importance of each topic. Moreover, we proposed a new measure (PSA) to select the optimal number of topics. The results showed that PSA performed better than the perplexity algorithm. We introduced an index called feature attention to measure the priority of all potential product features for the redesign. The local and global preferences of each feature are considered in feature attention. Lastly, a target features selection model was constructed by combining the various perspectives of both the customer and the manufacturer to select potential product features from all the features, considering engineering cost, environmental protection cost, redesign lead time, and carbon emission. The results from this study validate the effectiveness of DPFR when the focus is only on customer needs. Moreover, including the production constraints, DPFR can balance customer needs and the manufacturing constraints (such as redesign cost, redesign time, and carbon emissions) and can provide guidelines for practitioners to discover product features for the redesign.


### 5.1. Research Contributions

This study contributes to academia from three different perspectives.First, this study verifies that identifying helpful reviews can help manufacturers make strategic decisions regarding product improvement. Although most previous studies have also focused on identifying helpful reviews [17, 29, 40], they have not provided a fine-grained analysis of product improvement and redesign.Second, in this study,product topics for customers were generated from helpful reviews, and the influence of each topic was quantified. Previous studies have mainly focused on the polarity detection of product features [12, 14] and neglected the effect of different topics. In our study, a structured model was constructed to identify the topics and calculate the importance of the product features of each topic for product improvement.Third, this study combines customer satisfaction with the manufacturing constraints of product design. Previous studies mainly focused only on customer satisfaction [12, 19] and did not investigate a method for aligning the customers’ preferences, with manufacturing design. Moreover, our study proposes a feature selection model that considers economic and environmental factors to select to-be-improved features for product redesign.


### 5.2. Practical Implications

This study makes several practical and industrial contributions.First, we used a real dataset of a well-known short-video platform in China to test and validate the proposed method. The results show that our method can effectively identify potential features for product redesign.Second, our method is feasible for product redesign because it considers both customer satisfaction and the manufacturing constraints of product redesign. The selected potential features are usually product attributes that both customers and manufacturers are most concerned about. Therefore, decision makers can further understand the strengths and weaknesses of the product and make strategic decisions about product improvements.Third, manufacturers can use our method to develop an automatic software system connected to online platforms to constantly analyze real-time data from customers. Given the emergence of new media and globalization, it is vital for companies to rapidly track and deal with customer needs at an early stage to retain their competitive position in the market. Under these circumstances, our method will be an efficient tool.


### 5.3. Limitations and Future Work

This study has several limitations. First, the model for identifying helpful reviews requires a significant amount of data to be manually labeled. Future work needs to consider unsupervised learning techniques to reduce the manual labor required. Second, this study considers only economic and environmental constraints in the simplest form to construct an optimal configuration model for product features. However, in the actual production process of a company, the constraints are more complicated and interrelated. Therefore, future work should consider more complex constraints, such as combining economic constraints, environmental constraints, and corporate social responsibility constraints. Third, in the whole life cycle of product redesign, continuous learning techniques can be applied to learn and update knowledge constantly during product improvement [78, 79]. Furthermore, although this study was applied to one example product, it has the potential to be applied in many domains in future work.


## 5.1. Research Contributions

This study contributes to academia from three different perspectives.First, this study verifies that identifying helpful reviews can help manufacturers make strategic decisions regarding product improvement. Although most previous studies have also focused on identifying helpful reviews [17, 29, 40], they have not provided a fine-grained analysis of product improvement and redesign.Second, in this study,product topics for customers were generated from helpful reviews, and the influence of each topic was quantified. Previous studies have mainly focused on the polarity detection of product features [12, 14] and neglected the effect of different topics. In our study, a structured model was constructed to identify the topics and calculate the importance of the product features of each topic for product improvement.Third, this study combines customer satisfaction with the manufacturing constraints of product design. Previous studies mainly focused only on customer satisfaction [12, 19] and did not investigate a method for aligning the customers’ preferences, with manufacturing design. Moreover, our study proposes a feature selection model that considers economic and environmental factors to select to-be-improved features for product redesign.


## 5.2. Practical Implications

This study makes several practical and industrial contributions.First, we used a real dataset of a well-known short-video platform in China to test and validate the proposed method. The results show that our method can effectively identify potential features for product redesign.Second, our method is feasible for product redesign because it considers both customer satisfaction and the manufacturing constraints of product redesign. The selected potential features are usually product attributes that both customers and manufacturers are most concerned about. Therefore, decision makers can further understand the strengths and weaknesses of the product and make strategic decisions about product improvements.Third, manufacturers can use our method to develop an automatic software system connected to online platforms to constantly analyze real-time data from customers. Given the emergence of new media and globalization, it is vital for companies to rapidly track and deal with customer needs at an early stage to retain their competitive position in the market. Under these circumstances, our method will be an efficient tool.


## 5.3. Limitations and Future Work

This study has several limitations. First, the model for identifying helpful reviews requires a significant amount of data to be manually labeled. Future work needs to consider unsupervised learning techniques to reduce the manual labor required. Second, this study considers only economic and environmental constraints in the simplest form to construct an optimal configuration model for product features. However, in the actual production process of a company, the constraints are more complicated and interrelated. Therefore, future work should consider more complex constraints, such as combining economic constraints, environmental constraints, and corporate social responsibility constraints. Third, in the whole life cycle of product redesign, continuous learning techniques can be applied to learn and update knowledge constantly during product improvement [78, 79]. Furthermore, although this study was applied to one example product, it has the potential to be applied in many domains in future work.



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*Source: 7021431-2022-08-21.xml*