A Sentiment and Topic Model with Timeslice, User and Hashtag for Posts on Social Media

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 784)


Nowadays plenty of user-generated posts, e.g., tweets, are published on social media and the posts imply the public’s opinions towards various topics. Joint sentiment/topic models are widely applied in detecting sentiment-aware topics on the lengthy documents. However, the characteristics of posts, i.e., short texts, on social media pose new challenges: (1) context sparsity problem of posts makes traditional sentiment-topic models infeasible; (2) conventional sentiment-topic models are designed for flat documents without structure information, while publishing users, publishing timeslices and hashtags of posts provide rich structure information for modeling these posts. In this paper, we firstly devise a method to mine potential hashtags, based on explicit hashtags, to further enrich structure information for posts, then we propose a novel Sentiment Topic Model for Posts (STMP) which aggregates posts with the structure information, i.e., timeslices, users and hashtags, to alleviate the context sparsity problem. Experiments on Twitter7 show STMP outperforms previous models in sentiment-aware topic extraction.


Topic model Sentiment analysis Topic extraction Short text 



This work is supported in part by the National Natural Science Foundation of China (NSFC) under Grant No. 61672153, the 863 Program under Grant No. 2015AA015406 and the Fundamental Research Funds for the Central Universities and the Research Innovation Program for College Graduates of Jiangsu Province under Grant No. KYLX16_0295.


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© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceSoutheast UnversityNanjingChina

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