Authors,Title,Year,Abstract
“Lappeman J., Clark R., Evans J., Sierra-Rubia L.”,The effect of nWOM firestorms on South African retail banking,2020,“Purpose: This study analysed the effect of online negative word-of-mouth (nWOM) firestorms in the retail banking sector. By understanding negative sentiment and sentiment recovery across an entire retail banking sector, the research exposed a unique view of banking in South Africa. Design/methodology/approach: The study made use of both a sentiment and topic analysis of over 1.7 million social media posts in South Africa. The methodology made use of both NLP and human validation techniques to measure changes in social media sentiment during online firestorms. This measurement included each of South Africa’s major retail banks over a twelve month period. Findings: From the analysis, key trigger characteristics for these firestorms (product failures, service failures, social failures and communication failures) were categorised. In addition, the average duration of a firestorm was calculated and factors that impact sentiment recovery were explored. Originality/value: The study was located in South Africa and, unlike firm level studies, researched nWOM for the whole retail banking sector. A theoretical footprint depicting the typical anatomy of a firestorm was derived in order to aid stakeholders to be more vigilant and better equipped to provide correct intervention in such times of crisis. © 2020, Emerald Publishing Limited.”
“Budianto A.G., Wirjodirdjo B., Maflahah I., Kurnianingtyas D.”,Sentiment Analysis Model for KlikIndomaret Android App During Pandemic Using Vader and Transformers NLTK Library,2022,“COVID-19 has changed the Indonesian people’s shopping habits for consumer goods. The online retail application came as a response to social distancing and stay-at-home advice. KlikIndomaret is an online retail application that uses the omnichannel concept. As the number of downloads increased, the number of various comments and sentiments on that application also increased. In this study, the researcher did a sentiment analysis aimed to improve the quality of application experiences and retail services. The result of the analysis reflected the services given to customers thus far. The data included reviews and star ratings derived from 4,066 reviews which went under the process of data pre-processing. The methods used in this study were VADER and NLTK, improved by Transformer, without pre-training data. These methods could filter the users’ reviews with sarcasm tone. The results were sentiment labels that were appropriate based on the score comparison of positive and negative sentiments in one user’s review. This approach made the review sentiment process of thousands of data faster and more accurate. © 2022 IEEE.”
“Ahmed A.A.A., Agarwal S., Kurniawan I.M.G.A., Anantadjaya S.P.D., Krishnan C.”,Business boosting through sentiment analysis using Artificial Intelligence approach,2022,“In the recent years, Artificial Intelligence has conquered every field whether it is health sector, financial sector, satellite system, farming sector and many more. Artificial Intelligence has enhanced the performance of all these sectors. In this paper, the focus will be on business performance and the AI methods will be applied in the form of machine learning and deep learning. This paper will present how Artificial Intelligence has enhance the business through the sentiment analysis. The work has also discussed the sentiment analysis approach for the business applications. The paper has covered all the aspects with respect to artificial intelligence in the business domain with its advantages for enhancing the performance of the business. The work has also described the natural language processing for performing the sentiment analysis through which business performance can be boosted. © 2022, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.”
“Cirqueira D., Almeida F., Cakir G., Jacob A., Lobato F., Bezbradica M., Helfert M.”,Explainable Sentiment Analysis Application for Social Media Crisis Management in Retail,2020,“Sentiment Analysis techniques enable the automatic extraction of sentiment in social media data, including popular platforms as Twitter. For retailers and marketing analysts, such methods can support the understanding of customers’ attitudes towards brands, especially to handle crises that cause behavioural changes in customers, including the COVID-19 pandemic. However, with the increasing adoption of black-box machine learning-based techniques, transparency becomes a need for those stakeholders to understand why a given sentiment is predicted, which is rarely explored for retailers facing social media crises. This study develops an Explainable Sentiment Analysis (XSA) application for Twitter data, and proposes research propositions focused on evaluating such application in a hypothetical crisis management scenario. Particularly, we evaluate, through discussions and a simulated user experiment, the XSA support for understanding customer’s needs, as well as if marketing analysts would trust such an application for their decision-making processes. Results illustrate the XSA application can be effective in providing the most important words addressing customers sentiment out of individual tweets, as well as the potential to foster analysts’ confidence in such support. Copyright © 2020 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.”
“Pettersson T., Oucheikh R., Lofstrom T.”,NLP Cross-Domain Recognition of Retail Products,2022,“Self-checkout systems aim to provide a seamless and high-quality shopping experience and increase the profitability of stores. These advantages come with some challenges such as shrinkage loss. To overcome these challenges, automatic recognition of the purchased products is a potential solution. In this context, one of the big issues that emerge is the data shifting, which is caused by the difference between the environment in which the recognition model is trained and the environment in which the model is deployed. In this paper, we use transfer learning to handle the shift caused by the change of camera and lens or their position as well as critical factors, mainly lighting, reflection, and occlusion. We motivate the use of Natural Language Processing (NLP) techniques on textual data extracted from images instead of using image recognition to study the efficiency of transfer learning techniques. The results show that cross-domain NLP retail recognition using the BERT language model only results in a small reduction in performance between the source and target domain. Furthermore, a small number of additional training samples from the target domain improves the model to perform comparable as a model trained on the source domain. © 2022 ACM.”
“Tran A.D., Pallant J.I., Johnson L.W.”,Exploring the impact of chatbots on consumer sentiment and expectations in retail,2021,“Retailers are increasingly using conversational AI (chatbots) for customer service due to the perceived benefits and reduced operational costs of this emerging technology. Yet our understanding of how consumers perceive interactions with chatbots, and how these interactions may influence other consumer service programs, remains limited. This paper investigates the differences in consumers’ sentiments towards chatbots across retail sectors, and the influence chatbots have on consumers’ sentiments and expectations towards other service interactions with online human agents. Using a hybrid automated sentiment analysis approach, we identify that (1) overall sentiment towards bots are less negative than sentiment towards online human agents; (2) these sentiments differ across fashion and telecommunications sectors, and finally (3) sentiments towards online human agents in both sectors become more negative after a retailer implements a chatbot. © 2021 Elsevier Ltd”
“Mokadam A., Shivakumar S., Viswanathan V., Suresh M.A.”,Online product review analysis to automate the extraction of customer requirements,2021,“The increasing use of online retail platforms has generated an enormous amount of textual data on the user experiences with these products in online reviews. These reviews provide a rich resource to elicit customer requirements for a category of products. The recent research has explored this possibility to some extent. The study reported here investigates the coding of publically available user reviews to understand customer sentiments on environmentally-friendly products. The manual review typically consists of a qualitative analysis of textual content, which is a resource-intensive process. An automated procedure based on Aspect-Based Sentiment Analysis (ABSA) is proposed and explored in this study. This procedure can be beneficial in analyzing reviews of products that belong to a specific category. As a case study, environmentally-friendly products are used. Manual content analysis and automated ABSA-based analysis are performed on the same review data to extract customer sentiments. The results show that we obtain over a 50% classification accuracy for a multiclass classification NLP task with a very elementary word vector-based model. The drop in accuracy (compared to human annotation) can be offset because an automated system is thousands of times faster than a human. Given enough data, it will perform better than its human counterpart in tasks on customer requirement modeling. We also discuss the future routes that can be taken to extend our system by leveraging more sophisticated paradigms and substantially improving our system’s performance. Copyright © 2021 by ASME.”
“Kothamasu L.A., Kannan E.”,Sentiment analysis on twitter data based on spider monkey optimization and deep learning for future prediction of the brands,2022,“In this manuscript, a deep neural network is proposed by integrating improved adaptive-network-based fuzzy inference system (IANFIS) for branding online products to overcome these issues. Here, the sentiment analysis (SA) and prediction on future branding of products that are extracted from the twitter data is carried out. After the review process classifying the products as positive, negative, and neutral assessments completely concentrated in three folds, prediction of a future brand that is carried out by IANFIS for weighting the products finally classify them. This scheme helps the respective retailers/retail brands with their digital marketing team to understand their brand perception as opposed to others. The performance of the proposed method is compared with the existing methods, such as sentiment analysis on twitter data based on particle swarm optimization and genetic algorithm, sentiment analysis on twitter data based on particle swarm optimization and convolutional neural network, sentiment analysis on twitter data based on whale optimization algorithm and support vector machine, and sentiment analysis on twitter data based on convolutional neural network and long short term memory. The simulation results show that the proposed method outperforms the state of art methods. © 2022 John Wiley & Sons, Ltd.”
“Braga P., Brito P.Q., Roxo M.T.”,“Organisation, Classification and Analysis of Online Reviews Directed to Retail in the Municipality of Porto”,2022,“Web 2.0 has allowed collaboration, interaction and sharing of information online, such as online review platforms. Consequently, these short and straightforward opinions have increasingly proved to be essential sources of information not only for consumers but also for companies, as they represent the consumer’s sincere evaluation, free from any kind of bias. In this sense, there should be an interest in the analysis and monitoring of online reviews by companies, as the result of these actions may provide guidelines to readjust their strategy, support decision-making and ensure the satisfaction of their consumers. To generate useful information to assist decision-making and strategies’ implementation by retailers in the Municipality of Porto, online reviews from the GoogleMyBusiness platform were organised, classified, and analysed. 9945 online reviews were extracted, directed to 246 retail adaptations of the Municipality of Porto, from 2017 to 2020, which were later classified by the polarity of sentiment (positive, negative, neutral, or mixed). Sentiment analysis was conducted, combined with statistical tests and frequency distribution tables to discover relevant information for retailers. With sentiment analysis, retailers can understand their consumers and their behaviour to adapt their strategies and make the right decisions to ensure their customers’ satisfaction. With the results obtained, this study proves that it is possible to extract useful information from online reviews and reveals that it is still an area of little interest for retailers in the Municipality of Porto. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.”
“Loke R.E., Steentjes I.A.A.”,Assessing Corporate Reputation from Online Employee Reviews,2022,“The overarching aim of this paper is to define, develop and present a processing pipeline that has practical application for companies, meaning, being extendable, representative from marketing perspective, and reusable with high reliability for any new, unseen data that generates insights for evaluation of the reputation construct based on collected reviews for any (e.g. retail) organisation that is willing to analyse or improve its performance. First, determinant attributes have to be defined in order to generate insights for evaluation with respect to corporate reputation. Second, in order to generate insights data has to be collected and therefore a method has to be developed in order to extract online stakeholder data from reviews. Furthermore, a suitable algorithm has to be created to assess the extracted information based on the determinant attributes in order to analyse the data. Preliminary results indicate that application of our processing pipeline to online employee review data that are publicly available on the web is valid. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.”
“Han Y., Moghaddam M.”,Analysis of sentiment expressions for user-centered design,2021,“Devising intelligent systems capable of identifying the idiosyncratic needs of users at scale and translating them into attribute-level design feedback and recommendations is a key prerequisite for successful user-centered design processes. Recent studies show that 49% of design firms lack systems and tools for monitoring external platforms, and only 8% have adopted digital, data-driven approaches for new product development despite acknowledging them as a high priority. The state-of-the-art attribute-level sentiment analysis approaches based on deep learning have achieved promising results; however, these methods pose strict preconditions, require manually labeled data for training and pre-defined attributes by experts, and only classify sentiments intro predefined categories which have limited implications for designers. This article develops a rule-based methodology for extracting and analyzing the sentiment expressions of users on a large scale, from myriad reviews available on social media and e-commerce platforms. The methodology further advances current unsupervised attribute-level sentiment analysis approaches by enabling efficient identification and mapping of sentiment expressions of individual users onto their respective attributes. Experiments on a large dataset scraped from a major e-commerce retail store for apparel and indicate 74.3%–93.8% precision in extracting attribute-level sentiment expressions of users and demonstrate the feasibility and potentials of the developed methodology for large-scale need finding from user reviews. © 2021 Elsevier Ltd”
“Dastidar U.G., Ambekar S.S., Hudnurkar M., Lidbe A.D.”,Experiential retailing leveraged by data analytics,2021,“The purpose of the study is to establish how in retail industry consumer data can be leveraged and analysed to provide customers an enhance shopping experience. Popular machine learning algorithms related to text mining AIDS in parsing the natural language and helps to understand the brand image and what the brand currently is lacking. In the last decade, although ecommerce brought a revolution in retail industry, shopping trends show that consumers spend more in offline store than online. The rise of omni-channel retailing and data-driven decision-making are shifting retailer focus to providing enhanced in-store customer experiences. Retailers are trying to find ways to stand out in the highly competitive environment. The solution to this problem is providing retailtainment. This study helps to understand how the available customer data is to be analysed to create unique experiences and enable experience-based stores. The results of this study will help a retail company understand how omnichannel play an important role creating customer engagement strategies. © 2021 IGI Global. All rights reserved.”
“Agrawal S.R., Mittal D.”,Optimizing customer engagement content strategy in retail and E-tail: Available on online product review videos,2022,“The present study attempts to examine how the various emotions expressed in customer engagement content available on online product review videos reflect purchase intentions. The study identified 97 popular review videos of different technical products on YouTube, with a total of 93,181 comments posted between January 2018 and June 2021. After filtering, the final set of data consisted of 18,452 comments. The paper employed both Poisson and negative binomial regression to test the hypotheses and strengthen the findings. The study explores new insights for predicting purchase intentions of potential customers with the help of a four-factor model based on customer engagement content available on online product review videos. Thus, this research supports the applicability of MRT, CET and SOR theories in the existing literature on retailing and e-tailing by developing a four-factor model. A wide range of consumer responses, both quantitative and qualitative, have been taken into account to predict purchase intentions using sentiment analysis, which makes this study unique and robust. Additionally, the paper entails several significant managerial implications for retailing and e-tailing firms to reformulate their marketing strategies. © 2022 Elsevier Ltd”
“Ahmed C., ElKorany A., ElSayed E.”,Prediction of customer’s perception in social networks by integrating sentiment analysis and machine learning,2022,“Understanding the customer behavior and perception are important issues for motivating customer satisfaction in marketing analysis. Customer conversation with customer support services through social networks channel provides a wealth of information for understanding customer perception. Therefore, in this paper, a hybrid framework that integrated sentiment analysis and machine learning techniques is developed to analyze interactive conversations among customers and service providers in order to identify the change of polarity of such conversation. This framework aims to detect the conversation polarity switch as well as predict the sentiment of the end of the customer conversation with the service provider. This would help companies to improve customer satisfaction and enhance the customer engagement. The effectiveness of the proposed framework is measured by extracting a real dataset that expresses more than 5000 conversational threads between a customer service agent of an online retail service provider (AmazonHelp) and different customers using the retailer’s twitter public account for the duration of one month. Different classical and ensemble machine learning classifiers were applied, and the results showed that the decision trees outperformed all other techniques. © 2022, The Author(s).”
“Dadhich A., Thankachan B.”,Sentiment Analysis of Amazon Product Reviews Using Hybrid Rule-Based Approach,2022,“In the past few years, retail market industries have taken a broad form to sell the products online and also to give the opportunity to customers to provide their valuable feedbacks, suggestions and recommendations. The aim of this paper is to provide an automatic comment analyzer. And propose an automatic comment analyzer and classification system which can determine the polarity of the customer comments collected from Amazon and Flipkart data domains effectively. This system should be able to process the large number of reviews. It should categorize the comments as positive, negative and neutral classes using five prime supervised learning classifiers such as NB, LR, SentiWordNet, RF and KNN. The paper also discusses their experimental results and challenges found. Therefore, this study shows the maximum usage of feature extraction, positive–negative sentiment, Amazon web source, mobile phone for a large set of reviews in the existing algorithms. It included the preliminary definitions, information extraction and retrieval aspects, role of machine learning, and the comment mining. The comment analysis and classification described comment polarity, orientation, subjectivity detection, comment analysis, summarization and classification. The classification of comment analysis techniques explained various lexicons and supervised algorithms along with the essential used-based concerns. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.”
“Dutta A., Maitra J., Ghosh P., Chatterjee P., Goto T., Sen S.”,Categorization of the reviewers based on the preciseness of the consumers’ reviews in online retail marketplace,2022,“In the last decade, huge growth is observed in the online marketplace. Consumers are increasingly engaged in online shopping due to its operational flexibility, huge product search space and diversified products. In this virtual marketplace, consumers make the purchase decision not only based on product specifications given by the seller but also considers the product reviews given by the peer customers. On the other hand, inconsistent reviews make the consumers confused and thus have a negative impact on the overall sales of a business entity. Therefore, identifying the reviewers giving precise and consistent opinions about the products is important for business organizations. In this work, a novel methodology is suggested to classify reviewers into different categories based on the preciseness of the reviews. Business entities can utilize the precise reviewers for getting genuine product feedback and encourage this group to promote their products resulting in increased business growth. © 2022 ACM.”
“Nithuna S., Laseena C.A.”,Review on Implementation Techniques of Chatbot,2020,“Chatbots are an intelligent system being developed using artificial intelligence (AI) and natural language processing (NLP) algorithms. It successfully interfaces and answers the inquiries of the users. Dialogue/conversation operators are predominately utilized by organizations, government associations, and non-benefit associations. They are habitually conveyed by money related associations like a bank, charge card organizations, organizations like online retail locations and new businesses. These conversational specialists are utilized in organizations running from exceptionally small start-ups to enormous partnerships. There are numerous chatbot advancement systems accessible in the market both code-based and interface based. However, they don’t have the handiness and adaptability in growing real dialogues. Among popular intelligent personal assistants includes ‘Amazon’s Alexa, Microsoft’s Cortana and Google’s Google Assistant’. The elements of these specialists are restricted. The current chatbots are created utilizing rule-based techniques, simple machine learning algorithms or retrieval based techniques but they do not generate good results. This paper provides a critical review of chatbots and the current strategies are exhaustively explored and talked discussed. © 2020 IEEE.”
“Wu J.-J., Chang S.-T.”,Exploring customer sentiment regarding online retail services: A topic-based approach,2020,“User-generated content is a valuable source for understanding online shoppers’ emotions. Using text-mining techniques, this study identifies seven topics regarding online retail services in online posts: product, retailer promotion, delivery, payment, communication, return/refund, and price. The topics are associated with the sentiment polarity of online shoppers’ posts. This study further explores whether the emotional responses from domestic and cross-border online shoppers differ with regard to these topics. The results show that differences exist in these two groups’ sentiments concerning product and payment. Furthermore, there are differences in the two groups’ respective negative emotions (i.e., anger, sadness, and fear) concerning delivery, communication, and return/refund. The findings of this study provide online retailers with important managerial implications. © 2020”
“Han Y., Moghaddam M.”,Eliciting Attribute-Level User Needs from Online Reviews with Deep Language Models and Information Extraction,2021,“Eliciting user needs for individual components and features of a product or a service on a large scale is a key requirement for innovative design. Synthesizing data as an initial discovery phase of a design process is usually accomplished with a small number of participants, employing qualitative research methods such as observations, focus groups, and interviews. This leaves an entire swath of pertinent user behavior, preferences, and opinions not captured. Sentiment analysis is a key enabler for large-scale need finding from online user reviews generated on a regular basis. A major limitation of current sentiment analysis approaches used in design sciences, however, is the need for laborious labeling and annotation of large review datasets for training, which in turn hinders their scalability and transferability across different domains. This article proposes an efficient and scalable methodology for automated and large-scale elicitation of attribute-level user needs. The methodology builds on the state-of-The-Art pretrained deep language model, BERT (Bidirectional Encoder Representations from Transformers), with new convolutional net and named entity recognition (NER) layers for extracting attribute, description, and sentiment words from online user review corpora. The machine translation algorithm BLEU (BiLingual Evaluation Understudy) is utilized to extract need expressions in the form of predefined part-of-speech combinations (e.g., adjective-noun, verb-noun). Numerical experiments are conducted on a large dataset scraped from a major e-commerce retail store for apparel and footwear to demonstrate the performance, feasibility, and potentials of the developed methodology. © 2020 Royal Society of Chemistry. All rights reserved.”
“Aziz R.H.H., Dimililer N.”,Twitter Sentiment Analysis using an Ensemble Weighted Majority Vote Classifier,2020,“Sentiment analysis extracts the emotions expressed in text and has been employed in many fields including politics, elections, movies, retail businesses and in recent years microblogs to understand, track and control the human sentiments or reactions toward products events or ideas. Nevertheless challenges such as different styles of writing, use of negation and sarcasm, existence of spelling mistakes, invention of new words etc. provide obstacle in the correct classification of sentiments. This paper provides an ensemble of classifiers framework for sentiment analysis. The proposed weighted majority voting ensemble method combines six models including Naïve Bayes, Logistic Regression, Stochastic Gradient Descent, Random Forest, Decision Tree and Support Vector Machine to form a single classifier. Weights of the individual classifiers of the ensemble are chosen as accuracy or Fl-score by optimizing their performance. This approach combines models based on the simple majority voting as opposed to the one based on weighted majority voting. Additionally, a comparison is drawn among these six individual classifiers to evaluate their performance. The proposed ensemble model is tested on some existing sentiment datasets, including SemEval 2017 Task 4A, 4B and 4C. The results demonstrate that the Logistic Regression classifier is optimal as compared to other individual classifiers. Furthermore, the proposed ensemble weighted majority voting classifier with the six individual classifiers performs better compared to the simple majority voting and all independent classifiers. © 2020 IEEE.”
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