Investigating consumer preferences on product designs by analyzing opinions from social networks using evidential reasoning. (January 2020) - Ng, C.Y.; Law, Kris M.Y.
Highlights: An objective method to summarize the text comments extracted from social media. Uncertainty of different sentiment words used in a comment is remedied by fuzzy & ER. Weight elicitation processes are based on the opinion strengths of sentiment words. Preferences are assigned by sentiment scores for evaluating decision criteria. Abstract: The rapid growth of e-commerce and social networking sites has created various challenges for the extraction of user-generated content (UGC). In the era of big data, customer opinions from social media are utilized for investigating consumer preferences to support product redesigns. Opinion mining, including the various automatic text classification algorithms using sentiment analysis is a capable tool to deal with a large amount of comments on the social networking sites. In which, sentiment analysis is used to determine the contextual polarity within a comment by searching sentimental words. However, the inconsistency on choosing the sentiment words leads to the inaccurate interpretation of the opinion strength of sentiment words. An approach to summarize the UGC from social networking media using fuzzy and ER without the need to review all the comments is proposed in this paper. The inaccuracy on determination of the polarity of sentiment words and corresponding opinion strengths is rectified by fuzzy approximation and ER. The result is presented in ranking therefore the effort for result interpretation significantly reduced. The incorporation of sentiment analysis with ER to analyze the UGC for product designs is a new attempt in investigating consumer preferences. The proposed approach is shown to be handy, sufficient, and cost effective for the product design and re-design, particularly in the preliminary stage. This project can be further extended by employing alternative fuzzy approximate techniques in the fuzzy-ER approach to support the sentiment analysis to enhance the accuracy of sentiment values for determining the distribution assessments of ER.