A vector space model for automatic indexing
Communications of the ACM
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
A Topic Modeling Approach and Its Integration into the Random Walk Framework for Academic Search
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Normalizing SMS: are two metaphors better than one?
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
C-Feel-It: a sentiment analyzer for micro-blogs
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations
Democrats, republicans and starbucks afficionados: user classification in twitter
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards an on-line analysis of tweets processing
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
Extracting social events based on timeline and sentiment analysis in twitter corpus
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
PLEAD 2012: politics, elections and data
Proceedings of the 21st ACM international conference on Information and knowledge management
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Tweets exchanged over the Internet are an important source of information even if their characteristics make them difficult to analyze (e.g., a maximum of 140 characters; noisy data). In this paper, we address the problem of extracting relevant topics through tweets coming from different communities. More precisely we are interested to address the following question: which are the most relevant terms given a community. To answer this question we define and evaluate new variants of the traditional TF-IDF. Furthermore we also show that our measures are well suited to recommend a community affiliation to a new user. Experiments have been conducted on tweets collected during French Presidential and Legislative elections in 2012. The results underline the quality and the usefulness of our proposal.