Towards building a social emotion detection system for online news

• We study the problem of social emotion detection of online users in the news domain.
• A novel algorithm is proposed and devised for generating emotion lexicon and to predict emotions.
• Based on the above-mentioned algorithm, a hybrid approach combining document selection and POS is developed.
• A social emotion detection system is developed for online news.
• Extensive evaluations have been conducted to validate the performance of our approach vis-a-vis other existing ones.

Social emotion detection of online users has become an important task for mining public opinions. Social emotion detection aims at predicting the readers’ emotions evoked by news articles, tweets, etc. In this article, we focus on building a social emotion detection system for online news. The system is built based on the modules of document selection, Part-of-speech (POS) tagging, and social emotion lexicon generation. Empirical studies are extensively conducted on a large scale real-world collection of news articles. Experiments show that the document selection algorithm has a positive effect on the social emotion detection. The system performs better with the words and POS combination compared to a feature set consisting only of words. POS is also useful to detect emotion ambiguity of words and the context dependence of their sentiment orientations. Furthermore, the proposed method of generating the lexicon outperforms the baselines in terms of social emotion prediction.