ITMDID: An improved topic model for defect information derivation.

With the help of topic models, social media data have become a valuable information source for manufacturers to identify product defects. However, when absorbing information on product defects, special defect-unrelated texts that mention product failures induced by customers will be mistaken as defect-related by existing methods, leading to biased results. Furthermore, extant topic models suffer from the "topic-indiscriminative" problem, which means topics derived by topic models are similar. In order to address these problems, we first create a lexicon to differentiate whether the texts mentioning product failures are defect-related. Then we propose a topic model named Improved Topic Model for Defect Information Derivation (ITMDID) to derive product defect information from defect-related texts. To enhance the discrimination of extracted topics, we consider the word coherence when inferring variables of ITMDID via Gibbs sampling. Finally, we applied the developed approaches to analyze two case studies of the automobile and laptop industries. Experimental results prove that our methods can derive product defects from social media data more accurately and comprehensively than state-of-the-art approaches. [ABSTRACT FROM AUTHOR]