Building emotional dictionary for sentiment analysis of online news
World Wide WebPublished 7 June 2013
Yanghui Rao, Jingsheng Lei, Liu Wenyin, Qing Li, Mingliang Chen
Citations192
SJR quartileQ1
SJR score0.88
SNIP1.17
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TL;DR
An efficient algorithm and three pruning strategies are proposed to automatically build a word-level emotional dictionary for social emotion detection and a method based on topic modeling is proposed to construct a topic-level dictionary, where each topic is correlated with social emotions.
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.
Keywords
Computer Science
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