Affiliations: [a] School of Management, Hefei University of Technology, Hefei, Anhui, China | [b] Process Optimization and Intelligent Decision Making, Ministry of Education, Hefei, Anhui, China
Correspondence:
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Corresponding author: Chunhua Sun, School of Management, Hefei University of Technology, No. 193, Tunxi Road, Hefei, Anhui 230009, China. %****␣ida-26-ida205715_temp.tex␣Line␣50␣**** E-mail: sunchunhua@hfut.edu.cn.
Abstract: With the rapid development of social media and mobile Internet, short reviews, such as Weibo and Twitter, have exploded online. Discovering topics from short reviews is significant for many practical applications. It can effectively not only identify users’ attitudes and emotions but also enhance customer satisfaction and shopping experience. Because reviews are relatively short, the sparsity of reviews considerably restricts the quality of topic discovery. To improve the efficiency of topic discovery, we introduce the concept of data enhancement and strengthen the data in sentences and words in short reviews based on the weight of importance. We then propose a topic model for reviews to topic discovery based on data enhancement (shorted as DE-LDA). We verify the rationality and feasibility of DE-LDA on real datasets. Results show that the proposed method outperforms benchmarks in topic discovery and also has better clustering effects.
Keywords: Short reviews, topic discovery, data enhancement, clustering
