# Big Data and AI-Driven Product Design: A Survey
## 4 Product Design Based on Big Data and AI
### Product Design Based on Textual Data
Various unsupervised, semi-supervised, and supervised methods have been developed for implicit attribute extraction [129]. For instance, [PERSON] et al. [130] used an implicit topic model that incorporated pre-existing knowledge to select training attributes and developed an implicit attribute classifier based on SVM. To overcome the lack of a training corpus, they annotated a large number of online reviews. Meanwhile, [PERSON] et al. [131] proposed an unsupervised rule-based method that can extract both subjective and objective features, including implicit attributes, from customer reviews. Additionally, employing synonym dictionaries is a viable method. For example, [PERSON] et al. [109] combined WordNet and manually defined  
Figure 8: The typical processing flow of textual data. It includes five parts, namely data acquisition, data processing, information extraction, and application. synonyms to extract attributes of mobile phones. WordNet is an English lexical database that organizes words based on synonyms and antonyms. By analyzing online comments, designers can gain a more detailed understanding of users' attention to each product attribute at a finer granularity, enabling a more precise analysis. _Opinion mining._ In product design, opinion mining aims to extract descriptive words that express subjective opinions, commonly known as opinion words. There are two primary approaches to this task: co-occurrence analysis and syntactic analysis. The co-occurrence approach identifies opinion words by analyzing the adjectives that appear in proximity to product attributes. Meanwhile, the syntactic approach relies on analyzing the structure and dependency of review sentences. For example, [PERSON] and [PERSON] [131] categorized product attributes as either frequent or infrequent, with the former identified through POS and association mining, and nearby adjectives deemed opinion words. The latter were identified through a reverse search for opinion words. In addition to the two types of approaches discussed above, topic modeling has also shown promising results in opinion word extraction. For instance, [PERSON] et al. [97] used latent Dirichlet allocation (LDA) to extract customer satisfaction dimensions from online reviews and proposed the effect-based KANO model (EKM). LDA is a topic model that can group synonyms into the same topic and obtain its probability distribution. [PERSON] et al. [132] applied the long short-term memory (LSTM) model to extract opinion words from raw online reviews and mapped customer opinions to design parameters through deep learning. However, it is worth noting that the topic model ignores the fine-grained aspects. Several scholars have studied the expression of opinion words since different reviewers may share the same opinions but use different words to express them. In our previous research [133], we clustered similar opinion words based on word2 vec. Additionally, [PERSON] et al. [134] restructured raw sentences to extract attribute-opinions from online reviews, following grammatical rules. However, different languages have different sentence structure rules, so it is hard to expand to other languages. _Sentiment analysis._ In product design, sentiment analysis plays a crucial role in identifying customers' emotional attitudes towards a product and classifying them as positive, negative, or neutral. Sentiment analysis can be conducted at three different levels [135; 136; 137]: (i) document-level, which is a coarse-grained analysis of all reviews in a document; (ii) sentence-level, which is a medium-grained analysis of individual sentences; and (iii) aspect (attribute)-level, which is a fine-grained analysis of specific product attributes. In addition, sentiment analysis methods are categorized into three categories: machine learning methods, lexicon-based methods, and hybrid methods [117; 118; 138; 139].