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Building emotional dictionary for sentiment analysis of online news

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Abstract

Sentiment analysis of online documents such as news articles, blogs and microblogs has received increasing attention in recent years. In this article, we propose an efficient algorithm and three pruning strategies to automatically build a word-level emotional dictionary for social emotion detection. In the dictionary, each word is associated with the distribution on a series of human emotions. In addition, a method based on topic modeling is proposed to construct a topic-level dictionary, where each topic is correlated with social emotions. Experiment on the real-world data sets has validated the effectiveness and reliability of the methods. Compared with other lexicons, the dictionary generated using our approach is language-independent, fine-grained, and volume-unlimited. The generated dictionary has a wide range of applications, including predicting the emotional distribution of news articles, identifying social emotions on certain entities and news events.

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Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

    Yanghui Rao & Qing Li

  2. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China

    Jingsheng Lei

  3. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China

    Liu Wenyin

  4. Centre for Transport, Trade and Financial Studies, City University of Hong Kong, Kowloon, Hong Kong

    Mingliang Chen

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  1. Yanghui Rao
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  2. Jingsheng Lei
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  3. Liu Wenyin
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  4. Qing Li
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  5. Mingliang Chen
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Correspondence to Jingsheng Lei.

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Rao, Y., Lei, J., Wenyin, L. et al. Building emotional dictionary for sentiment analysis of online news. World Wide Web 17, 723–742 (2014). https://doi.org/10.1007/s11280-013-0221-9

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  • DOI: https://doi.org/10.1007/s11280-013-0221-9

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