Social sentiment sensor: a visualization system for topic detection and topic sentiment analysis on microblog

Abstract

As a new form of social media, microblogging provides platform sharing, wherein users can share their feelings and ideas on certain topics. Bursty topics from microblogs are the results of the emerging issues that instantly attract more followers and more attention online, which provide a unique opportunity to gauge the relation between expressed public sentiment and hot topics. This paper presents a Social Sentiment Sensor (SSS) system on Sina Weibo to detect daily hot topics and analyze the sentiment distributions toward these topics. SSS includes two main techniques, namely, hot topic detection and topic-oriented sentiment analysis. Hot topic detection aims to detect the most popular topics online based on the following steps, topic detection, topic clustering, and topic popularity ranking. We extracted topics from the hashtags using a hashtag filtering model because they can cover almost all the topics. Then, we cluster the topics that describe the same issue, and rank the topic clusters via their popularity to exploit the final hot topics. Topic-oriented sentiment analysis aims to analyze public opinions toward the hot topics. After retrieving the topic-related messages, we recognize sentiment for each message using a state-of-the-art SVM (Support Vector Machine) sentiment classifier. Then, we summarize the sentiments for the hot topic to achieve topic sentiment distribution. Based on the above framework and algorithms, SSS produces a real-time visualization system to monitor social sentiments, which is offering the public a new and timely perspective on the dynamics of the social topics.

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Notes

  1. 1.

    http://en.wikipedia.org/wiki/Sina_Weibo

  2. 2.

    http://www.csc.ncsu.edu/faculty/healey/tweet_viz/tweet_app/

  3. 3.

    http://www.socialmention.com/

  4. 4.

    http://gana.nlsde.buaa.edu.cn/hourly_happy/moodlens.html

  5. 5.

    Top Baidu can get the hot topics everyday from the queries. http://top.baidu.com/?vit=1.

  6. 6.

    We manually construct a dictionary that records the words that are irrelevant to the meaningful topics. This dictionary contains 381 words.

  7. 7.

    We manually construct a dictionary that records the words that always appearing in the ads. The dictionary contains 412 words.

  8. 8.

    http://news.sina.com.cn/

  9. 9.

    Because the proposed features of [16] are used for English tweets, some of them are not suitable for Chinese microblogs.

  10. 10.

    http://www.csie.ntu.edu.tw/~cjlin/liblinear/

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Acknowledgments

We thank the anonymous reviewers for their helpful comments. This work was supported by National Natural Science Foundation of China (NSFC) via grant 2014CB340506, 61300113 and 61273321, and the Ministry of Education Research of Social Sciences Youth funded projects via grant 12YJCZH304.

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Correspondence to Yanyan Zhao.

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Zhao, Y., Qin, B., Liu, T. et al. Social sentiment sensor: a visualization system for topic detection and topic sentiment analysis on microblog. Multimed Tools Appl 75, 8843–8860 (2016). https://doi.org/10.1007/s11042-014-2184-y

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Keywords

  • Sentiment analysis
  • Social media
  • Topic detection
  • Microblogging
  • Opinion mining