Next Article in Journal
Experimental Study on the Utility and Future of Collaborative Consumption Platforms Offering Tourism Related Services
Previous Article in Journal
Environmental-Based Speed Recommendation for Future Smart Cars
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle

Topic-Specific Emotion Mining Model for Online Comments

Shanghai Institute for Advanced Communication and Data Science, School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Author to whom correspondence should be addressed.
Future Internet 2019, 11(3), 79;
Received: 31 January 2019 / Revised: 17 March 2019 / Accepted: 21 March 2019 / Published: 24 March 2019
PDF [6367 KB, uploaded 28 March 2019]


Nowadays, massive texts are generated on the web, which contain a variety of viewpoints, attitudes, and emotions for products and services. Subjective information mining of online comments is vital for enterprises to improve their products or services and for consumers to make purchase decisions. Various effective methods, the mainstream one of which is the topic model, have been put forward to solve this problem. Although most of topic models can mine the topic-level emotion of the product comments, they do not consider interword relations and the number of topics determined adaptively, which leads to poor comprehensibility, high time requirement, and low accuracy. To solve the above problems, this paper proposes an unsupervised Topic-Specific Emotion Mining Model (TSEM), which adds corresponding relationship between aspect words and opinion words to express comments as a bag of aspect–opinion pairs. On one hand, the rich semantic information obtained by adding interword relationship can enhance the comprehensibility of results. On the other hand, text dimensions reduced by adding relationships can cut the computation time. In addition, the number of topics in our model is adaptively determined by calculating perplexity to improve the emotion accuracy of the topic level. Our experiments using Taobao commodity comments achieve better results than baseline models in terms of accuracy, computation time, and comprehensibility. Therefore, our proposed model can be effectively applied to online comment emotion mining tasks. View Full-Text
Keywords: topic-specific emotion mining; online comment; topic model topic-specific emotion mining; online comment; topic model

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Luo, X.; Yi, Y. Topic-Specific Emotion Mining Model for Online Comments. Future Internet 2019, 11, 79.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Future Internet EISSN 1999-5903 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top