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1
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79960063297
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Semantic enrichment of twitter posts for user profile construction on the social web
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Springer Berlin, Heidelberg
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Abel, F.1
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2
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84856343452
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Sentiment analysis of Twitter data
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A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity
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What’s in twitter, I know what parties are popular and who you are supporting now!
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Emotional states associated with music: classification, prediction of changes, and consideration in recommendation
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Heuristic semantic walk: browsing a collaborative network with a search engine-based heuristic
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Heuristic semantic walk for concept chaining in collaborative networks
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Heuristics for semantic path search in Wikipedia
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A sentiment-based approach to twitter user recommendation
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ACM Press, June 2013
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Gurini, D.F., Gasparetti, F., Micarelli, A. and Sansonetti, G. (2013), “A sentiment-based approach to twitter user recommendation”, Proceedings of 5th ACM RECSYS Workshop on Recommender Systems and the Social Web, ACM Press, June 2013.
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Proceedings of 5th ACM RECSYS Workshop on Recommender Systems and the Social Web
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Gurini, D.F.1
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10
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43449135033
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Why we Twitter: understanding microblogging usage and communities
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SanJose, CA: August
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Java, A.1
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84907337360
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Sentiment analysis of short informal texts
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Twitter trending topic classification
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December
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Mangal, N., Niyogi, R. and Milani, A. (2016), “Analysis of users’ interest based on tweets”, International Conference on Computational Science and Applications, ICCSA 2016, Lecture Notes in Computer Science, Vol. 9790, pp. 12-23.
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Analysis of users’ interest based on tweets
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The Stanford CORENLP natural language processing toolkit
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Association for Computational Linguistics
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16
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85027149767
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Clustering facebook for biased context extraction
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37249003940
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Planning in reactive environments
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Milani, A. and Poggioni, V. (2007), “Planning in reactive environments”, Computational Intelligence, Vol. 23 No. 4, pp. 439-463.
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Computational Intelligence
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Milani, A.1
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84864438709
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Community of scientist optimization: an autonomy oriented approach to distributed optimization
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Milani, A. and Santucci, V. (2012), “Community of scientist optimization: an autonomy oriented approach to distributed optimization”, AI Communications, Vol. 25 No. 2, pp. 157-172.
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A machine learning approach to twitter user classification
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AAAI Press
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Pennacchiotti, M. and Popescu, A. (2011), “A machine learning approach to twitter user classification”, Proceedings of the Fifth ICWSM, AAAI Press, pp. 281-288.
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Proceedings of the Fifth ICWSM
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Automated classification of book blurbs according to the emotional tags of the Social Network Zazie
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Sentiment analysis: a combined approach
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Algebraic differential evolution algorithm for the permutation flowshop scheduling problem with total flowtime criterion
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IEEE Transactions on Evolutionary Computation
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Recursive deep models for semantic compositionality over a sentiment treebank
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Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y. and Potts, C. (2013), “Recursive deep models for semantic compositionality over a sentiment treebank”, Proceedings Of The Conference on Empirical Methods in Natural Language Processing, pp. 1631-1642.
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Heart and soul sentiment strength detection in the social web with sentistrength
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Springer 2016
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Thelwall, M. (2013), “Heart and soul sentiment strength detection in the social web with sentistrength”, Proceedings of the CyberEmotions, Springer 2016, pp. 119-134.
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Proceedings of the CyberEmotions
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Predicting Elections with Twitter: What 140 characters reveal about political sentiment
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Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, AAAI Press
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Tumasjan, A.1
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26
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77954433229
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A bidirectional heuristic search for web service composition with costs
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Ukey, N., Niyogi, R., Singh, K., Milani, A. and Poggioni, V. (2010), “A bidirectional heuristic search for web service composition with costs”, International Journal of Web and Grid Services, Vol. 6 No. 2, pp. 160-175.
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International Journal of Web and Grid Services
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84904661038
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Probabilistic aspect mining model for drug reviews
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Leung, C.H.C., Liu, J. and Milani, A. (2014), “Probabilistic aspect mining model for drug reviews”, IEEE Transact\Ions on Knowledge and Data Engineering, Vol. 26 No. 8, pp. 2002-2013.
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IEEE Transact\Ions on Knowledge and Data Engineering
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0036540699
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NMDS codes of maximal length over fq, 8? q? 11
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Marcugini, S., Milani, A. and Pambianco, F. (2002), “NMDS codes of maximal length over fq, 8? q? 11”, IEEE Transactions on Information Theory, Vol. 48 No. 4, pp. 963-966.
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IEEE Transactions on Information Theory
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