# Big Data and AI-Driven Product Design: A Survey
## 4 Product Design Based on Big Data and AI
### Product Design Based on Textual Data  
The text exists in every phase of a product's lifecycle. Among them, user experience feedback after purchasing the product is particularly valuable for product design. In recent years, more and more users have shared their opinions on Twitter, Facebook, microblogs, blogs, and e-commerce websites [86; 87; 88; 89; 90; 91; 92]. As online reviews on e-commerce websites are provided by consumers who have purchased the product, they are considered highly reliable and authentic [93]. Research shows that 90 % of customers use online reviews as a reference, with 81% finding them helpful for purchase decisions, and 45% consulting them even while shopping in physical stores [94; 95]. Consequently, online reviews have become an important influence factor in consumer behavior and a reference for enterprises to use to improve their products [96; 22]. Online reviews offer many benefits, including understanding customer satisfaction [97; 98; 99; 100; 101], capturing user requirements [102; 103; 104], finding product deficiencies [105; 106; 107], proposing improvement strategies [65], comparing competitive products [108], and providing product recommendations [109; 110; 111; 112; 113; 114]. For instance, in order to identify innovation sentences from online reviews, [PERSON] et al. [4] proposed a deep learning-based approach. [PERSON] et al. [115, 116, 117, 118] focused on filtering out helpful reviews. [PERSON] et al. [118] presented a heuristic deep learning method to extract opinions and classified them into seven pairs of affective attributes, namely \"like-dislike\", \"aesthetic-inaesthetic\", \"soft-hard\", \"small-big\", \"useful-useless\", \"reliable-unreliable\", and \"recommended-not recommended\", [PERSON] et al. [119] combined reviews and electroencephalgrams signals to predict product ratings, and [PERSON] et al. [95] proposed a marginal effect-based KANO model (MEKM) to categorize customer requirements. [PERSON] et al. [118] explored product customization based on online reviews. Figure 8 summarizes the typical processing flow of textual data. The rapid development of NLP is the key to product design based on textual data, which including topic extraction, opinion mining, text classification, sentiment analysis, and text clustering. Product attributes extractionIn the field of product design, topic extraction can be used to extract product attributes (e.g., the screen, battery, weight, and camera of a smartphone) and opinions [111]. As each product has multiple attributes, and consumers have varying preferences and evaluations for each attribute, extracting attributes from online reviews becomes essential [111]. Typically, product attributes are nouns or noun phrases in online review sentences [113]. To extract product attributes, most studies utilize the part-of-speech (POS) tag to generate tags identifying whether a word is a noun, adjective, adverb, etc., and consider all nouns and noun phrases as attribute candidates. These candidates are then pruned using techniques such as term-frequency (TF) [117, 118], TF-IDF [124, 125], dictionary [126], manual definition [100], and clustering [127, 17]. Moreover, since product attributes are domain-sensitive, some studies consider it a domain-specific entity recognition problem. For instance, [PERSON] and [PERSON] [128] used named entity recognition (NER) to extract product attributes. The methods discussed above are limited to extracting explicit attributes and are not suitable for implicit attributes. The distinction between explicit and implicit attributes lies in whether they are explicitly mentioned or not. For example, in the sentence \"lap-top battery is durable, the laptop is expensive\". The \"battery\" is explicitly mentioned as an explicit attribute, while \"expensive\" is related to price and is an implicit attribute. Explicit attributes can be easily identified using rule-based or machine learning-based methods, implicit attributes require more sophisticated techniques [17].