Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Detecting controversial events from twitter
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Event discovery in social media feeds
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
French presidential elections: what are the most efficient measures for tweets?
Proceedings of the first edition workshop on Politics, elections and data
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We propose a novel method for extracting social events based on timeline and sentiment analysis from social streams such as Twitter. When a big social issue or event occurs, it tends to dramatically increase in the number of tweets. Users write tweets to express their opinions. Our method uses these timeline and sentiment properties of social media streams to extract social events. On timelines term significance is calculated based on Chi-square measure. Evaluating the method on Korean tweet collection for 30 events, our method achieved 94.3% in average precision in the top 10 extracted events. The result indicates that our method is effective for social event extraction.