TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Emerging topic detection on Twitter based on temporal and social terms evaluation
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Streaming first story detection with application to Twitter
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
You are where you tweet: a content-based approach to geo-locating twitter users
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Twitinfo: aggregating and visualizing microblogs for event exploration
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Sentiment analysis of Twitter data
LSM '11 Proceedings of the Workshop on Languages in Social Media
Text-mining the voice of the people
Communications of the ACM
Correlating financial time series with micro-blogging activity
Proceedings of the fifth ACM international conference on Web search and data mining
Automatic string replace by examples
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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The huge and ever increasing amount of text generated by Twitter users everyday embeds a wealth of information, in particular, about themes that become suddenly relevant to many users as well as about the sentiment polarity that users tend to associate with these themes. In this paper, we exploit both these opportunities and propose a method for: (i) detecting novel popular themes, i.e. events, (ii) summarizing these events by means of a concise yet meaningful representation, and (iii) assessing the prevalent sentiment polarity associated with each event, i.e., positive vs. negative. Our method is fully unsupervised and requires only a precompiled topic description in the form of set of potentially relevant keywords that might appear in the events of interest. We validate our proposal on a real corpus of about 8,000,000 tweets, by detecting, classifying and summarizing events related to three wide topics associated with tech-related brands.