Analyzing the sentiment orientation of each product aspect/feature might be sufficient to assist the customer to make purchase/usage decisions, but such level of information obtained by sentiment analysis is not detailed enough to assist the company in making product improvement or design decisions. Therefore, this paper aims to propose a novel method to extract more detailed information of the product.
This paper proposed to use a set of trivial lexical-Part-of-Speech patterns to prepare candidate corpus and then adopted a topic model to find the optimal number of topics and get the words distributions in each topic. Finally, combined a priori analysis and compactness rules, the authors found out the expected strong rules in each topic, which make up the final problems.
Experimental results on a real-life data set from Xiaomi forum showed the proposed method can extract the product problems effectively. The authors also explained the errors of experiment, which suggested the direction for future research.
This paper proposed a novel method to obtain information of product problems in detail, which will be useful to assist companies to improve their product performance.
