Tweet sentiment: From classification to quantification

Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015

Volumn , Issue , 2015, Pages 97-104

  Gao, Wei ^a   Sebastiani, Fabrizio ^a  

^a QATAR COMPUTING RESEARCH INSTITUTE   (Qatar)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); SOCIAL SCIENCES; SOCIAL SCIENCES COMPUTING;

ENABLING TECHNOLOGIES; EVALUATION MEASURES; FREQUENCY ESTIMATES; MARKET RESEARCHES; POLITICAL PARTIES; RECENT RESEARCHES; RELATIVE FREQUENCIES; SENTIMENT CLASSIFICATION;

LEARNING ALGORITHMS;

EID: 84962580356     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2808797.2809327     Document Type: Conference Paper

References (38)

1. Sitaram Asur and Bernardo A. Huberman. Predicting the future with social media. In Proceedings of the 10th IEEE/WIC/ACM International Conference on Web Intelligence (WI 2010), pages 492-499, Toronto, CA, 2010.
2. Jose Barranquero, Jorge Díez, and Juan José del Coz. Quantification-oriented learning based on reliable classifiers. Pattern Recognition, 48(2):591-604, 2015.
3. Jose Barranquero, Pablo González, Jorge Díez, and Juan José del Coz. On the study of nearest neighbor algorithms for prevalence estimation in binary problems. Pattern Recognition, 46(2):472-482, 2013.
4. Antonio Bella, Cèsar Ferri, José Hernández-Orallo, and María José Ramírez-Quintana. Quantification via probability estimators. In Proceedings of the 11th IEEE International Conference on Data Mining (ICDM 2010), pages 737-742, Sydney, AU, 2010.
5. Johan Bollen, Huina Mao, and Xiao-Jun Zeng. Twitter mood predicts the stock market. Journal of Computational Science, 2(1):1-8, 2011.
6. Javier Borge-Holthoefer, Walid Magdy, Kareem Darwish, and Ingmar Weber. Content and network dynamics behind Egyptian political polarization on Twitter. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2015), pages 700-711, Vancouver, CA, 2015.
7. S. Burton and A. Soboleva. Interactive or reactive? Marketing with Twitter. Journal of Consumer Marketing, 28(7):491-499, 2011.
8. Yee Seng Chan and Hwee Tou Ng. Estimating class priors in domain adaptation for word sense disambiguation. In Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics (ACL 2006), pages 89-96, Sydney, AU, 2006.
9. Chih-Chung Chang and Chih-Jen Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 2011.
10. Thomas M. Cover and Joy A. Thomas. Elements of information theory. John Wiley & Sons, New York, US, 1991.
11. Imre Csiszár and Paul C. Shields. Information theory and statistics: A tutorial. Foundations and Trends in Communications and Information Theory, 1(4):417-528, 2004.
12. Peter S. Dodds, Kameron D. Harris, Isabel M. Kloumann, Catherine A. Bliss, and Christopher M. Danforth. Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter. PLoS ONE, 6(12), 2011.
13. Andrea Esuli and Fabrizio Sebastiani. Optimizing text quantifiers for multivariate loss functions. ACM Transactions on Knowledge Discovery and Data, 9(4):1-27, 2015.
14. George Forman. Counting positives accurately despite inaccurate classification. In Proceedings of the 16th European Conference on Machine Learning (ECML 2005), pages 564-575, Porto, PT, 2005.
15. George Forman. Quantifying counts and costs via classification. Data Mining and Knowledge Discovery, 17(2):164-206, 2008.
16. Víctor González-Castro, Rocío Alaiz-Rodríguez, and Enrique Alegre. Class distribution estimation based on the Hellinger distance. Information Sciences, 218:146-164, 2013.
17. Benjamin Herfort, Svend-Jonas Schelhorn, Jõao P. de Albuquerque, and Alexander Zipf. Does the spatiotemporal distribution of tweets match the spatiotemporal distribution of flood phenomena? A study about the river Elbe flood in June 2013. In Proceedings of the 11th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2014), pages 747-751, Philadelphia, US, 2014.
18. Daniel J. Hopkins and Gary King. A method of automated nonparametric content analysis for social science. American Journal of Political Science, 54(1):229-247, 2010.
19. Thorsten Joachims. A support vector method for multivariate performance measures. In Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), pages 377-384, Bonn, DE, 2005.
20. Thorsten Joachims, Thomas Hofmann, Yisong Yue, and Chun-Nam Yu. Predicting structured objects with support vector machines. Communications of the ACM, 52(11):97-104, 2009.
21. Mesut Kaya, Guven Fidan, and Ismail Hakki Toroslu. Transfer learning using Twitter data for improving sentiment classification of Turkish political news. In Proceedings of the 28th International Symposium on Computer and Information Sciences (ISCIS 2013), pages 139-148, Paris, FR, 2013.
22. Gary King and Ying Lu. Verbal autopsy methods with multiple causes of death. Statistical Science, 23(1):78-91, 2008.
23. Svetlana Kiritchenko, Xiaodan Zhu, and Saif M. Mohammad. Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research, 50:723-762, 2014.
24. Micol Marchetti-Bowick and Nathanael Chambers. Learning for microblogs with distant supervision: Political forecasting with Twitter. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012), pages 603-612, Avignon, FR, 2012.
25. Eugenio Martínez-Cámara, Maria Teresa Martín-Valdivia, Luis Alfonso Ureña López, and Arturo Montejo Ráez. Sentiment analysis in Twitter. Natural Language Engineering, 20(1):1-28, 2014.
26. Letizia Milli, Anna Monreale, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi, and Fabrizio Sebastiani. Quantification trees. In Proceedings of the 13th IEEE International Conference on Data Mining (ICDM 2013), pages 528-536, Dallas, US, 2013.
27. Saif M. Mohammad, Svetlana Kiritchenko, and Xiaodan Zhu. NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets. In Proceedings of the 7th International Workshop on Semantic Evaluation (SemEval 2013), pages 321-327, Atlanta, US, 2013.
28. Preslav Nakov, Sara Rosenthal, Zornitsa Kozareva, Veselin Stoyanov, Alan Ritter, and Theresa Wilson. SemEval-2013 Task 2: Sentiment analysis in Twitter. In Proceedings of the 7th International Workshop on Semantic Evaluation (SemEval 2013), pages 312-320, Atlanta, US, 2013.
29. Brendan O'Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A. Smith. From tweets to polls: Linking text sentiment to public opinion time series. In Proceedings of the 4th AAAI Conference on Weblogs and Social Media (ICWSM 2010), Washington, US, 2010.
30. Alexandra Olteanu, Sarah Vieweg, and Carlos Castillo. What to expect when the unexpected happens: Social media communications across crises. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2015), pages 994-1009, Vancouver, CA, 2015.
31. Muhammad A. Qureshi, Colm O'Riordan, and Gabriella Pasi. Clustering with error estimation for monitoring reputation of companies on Twitter. In Proceedings of the 9th Asia Information Retrieval Societies Conference (AIRS 2013), pages 170-180, Singapore, SN, 2013.
32. Sara Rosenthal, Preslav Nakov, Svetlana Kiritchenko, Saif Mohammad, Alan Ritter, and Veselin Stoyanov. SemEval-2015 Task 10: Sentiment analysis in Twitter. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 451-463, Denver, US, 2015.
33. Sara Rosenthal, Alan Ritter, Preslav Nakov, and Veselin Stoyanov. SemEval-2014 Task 9: Sentiment analysis in Twitter. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 73-80, Dublin, IE, 2014.
34. Hassan Saif, Miriam Fernez, Yulan He, and Harith Alani. Evaluation datasets for Twitter sentiment analysis: A survey and a new dataset, the STS-Gold. In Proceedings of the 1st International Workshop on Emotion and Sentiment in Social and Expressive Media (ESSEM 2013), pages 9-21, Torino, IT, 2013.
35. Lidia Sánchez, Víctor González, Enrique Alegre, and Rocío Alaiz. Classification and quantification based on image analysis for sperm samples with uncertain damaged/intact cell proportions. In Proceedings of the 5th International Conference on Image Analysis and Recognition (ICIAR 2008), pages 827-836, Póvoa de Varzim, PT, 2008.
36. Tetsuro Takahashi, Shuya Abe, and Nobuyuki Igata. Can Twitter be an alternative of real-world sensors? In Proceedings of the 14th International Conference on Human-Computer Interaction (HCI International 2011), pages 240-249, Orlando, US, 2011.
37. Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, and Yasemin Altun. Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research, 6:1453-1484, 2005.
38. Xiaodan Zhu, Svetlana Kiritchenko, and Saif M. Mohammad. NRC-Canada-2014: Recent improvements in the sentiment analysis of tweets. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 443-447, Dublin, IE, 2014.