Using hashtags to capture fine emotion categories from tweets

Computational Intelligence

Volumn 31, Issue 2, 2015, Pages 301-326

  Mohammad, Saif M ^a   Kiritchenko, Svetlana ^a  

^a NATIONAL RESEARCH COUNCIL CANADA   (Canada)

Author keywords

affect; basic emotions; Big 5 model; hashtags; personality detection; sentiment analysis; social media; tweets; word emotion associations

Indexed keywords

SOCIAL NETWORKING (ONLINE);

AFFECT; BASIC EMOTIONS; HASHTAGS; PERSONALITY DETECTIONS; SOCIAL MEDIA; TWEETS;

SENTIMENT ANALYSIS;

EID: 84929049839     PISSN: 08247935     EISSN: 14678640     Source Type: Journal    
DOI: 10.1111/coin.12024     Document Type: Article

References (63)

1. Alm, C. O,., and, R. Sproat,. 2005. Emotional sequencing and development in fairy tales. In Proceedings of the First International Conference on Affective Computing and Intelligent Interaction, Beijing, China, pp. 668-674.
2. Alm, C. O,., and, D. Roth, and, R. Sproat,. 2005. Emotions from text: Machine learning for textbased emotion prediction. In Proceedings of the Joint Conference on HLT-EMNLP, Vancouver, Canada, pp. 579-586.
3. Aman, S,., and, S. Szpakowicz,. 2007. Identifying expressions of emotion in text. In Text, Speech and Dialogue, vol. 4629, Edited by, V. Matoušek, and, P. Mautner, Lecture Notes in Computer Science. Springer: Berlin / Heidelberg, pp. 196-205.
4. Blitzer, J,., M. Dredze, and, F. Pereira,. 2007. Biographies, Bollywood, boomboxes and blenders: Domain adaptation for sentiment classification. In Proceedings of the Conference of the Association for Computational Linguistics (ACL), Prague, Czech Republic, pp. 187-205.
5. Bollen, J,., H. Mao, and, A. Pepe,. 2011. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, Spain, pp. 450-453.
6. Boucouvalas, A. C,. 2002. Real time text-to-emotion engine for expressive Internet communication. Emerging Communication: Studies on New Technologies and Practices in Communication, 5: 305-318.
7. Bougie, J. R. G,., R. Pieters, and, M. Zeelenberg,. 2003. Angry customers don't come back, they get back: the experience and behavioral implications of anger and dissatisfaction in services: Open Access publications from Tilburg University: Tilburg, the Netherlands.
8. Chaffar, S., and, D. Inkpen,. 2011. Using a heterogeneous dataset for emotion analysis in text. In Canadian Conference on AI, St. John's, Canada, pp. 62-67.
9. Chang, C. -C,., and, C. -J Lin,. 2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2: 27:1-27:27.
10. Cherry, C,., S. M. Mohammad, and, B. de Bruijn,. 2012 Binary classifiers and latent sequence models for emotion detection in suicide notes. Biomedical Informatics Insights, 5: 147-154.
11. Daumé, H., 2007. Frustratingly easy domain adaptation. In Proceedings of the Conference of the Association for Computational Linguistics (ACL), Prague, Czech Republic, pp. 256-263.
12. Eisenstein, J., and, O'Connor, B,., and, Smith, N. A,., and, E. P. Xing,. 2010. A latent variable model for geographic lexical variation. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Cambridge, MA, pp. 1277-1287.
13. Ekman, P., 1992. An argument for basic emotions. Cognition and Emotion, 6 (3): 169-200.
14. Francisco, V,., and, P. Gervás,. 2006. Automated mark up of affective information in English texts. In Text, Speech and Dialogue, vol. 4188, Lecture Notes in Computer Science. Springer: Berlin / Heidelberg
15. Ganchev, K,., and, Graça, J,., Blitzer, J., and, B. Taskar,. 2012). Multi-view learning over structured and non-identical outputs. Report number UAI-P-2008-PG-204-211. Available at: http://arxiv.org/abs/1206.3256.
16. Genereux, M,., and, R. P. Evans,. 2006. Distinguishing affective states in weblogs. In AAAI-2006 Spring Symposium on Computational Approaches to Analysing Weblogs, Stanford, CA, pp. 27-29.
17. Gill, A., and, J. Oberlander,. 2002. Taking care of the linguistic features of extraversion. In Proceedings of the Conference of the Cognitive Science Society, Sapporo, Japan, pp. 363-368.
18. Go, A., and, Bhayani, R., and, Huang, L., 2009. Twitter Sentiment Classification using Distant Supervision. In Final Projects from CS224N for Spring 2008/2009 at The Stanford Natural Language Processing Group, Stanford, CA, pp. 1-12.
19. González-Ibáñez, R., and, Muresan, S., and, Wacholder, N., 2011. Identifying sarcasm in Twitter: A closer look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, OR, pp. 581-586.
20. Hall, M., and, Frank, E., and, Holmes, G., and, Pfahringer, B., and, Reutemann, P., and, Witten, I. H., 2009. The WEKA data mining software: An update. SIGKDD Explorations, 11: 10-18.
21. Hayes, N., and, Joseph, S., 2003. Big 5 correlates of three measures of subjective well-being. Personality and Individual Differences, 34 (4): 723-727.
22. Holzman, L. E., and, Pottenger, W. M., 2003. Classification of emotions in Internet chat: An application of machine learning using speech phonemes,Technical Report, Lehigh University, Bethlehem, PA.
23. John, D., and, Boucouvalas, A. C., and, Xu, Z., 2006. Representing emotional momentum within expressive internet communication. In Proceedings of the 24th IASTED International Conference on Internet and Multimedia Systems and Applications, St. Thomas, U.S. Virgin Islands, pp. 183-188.
24. John, O. P., and, Srivastava, S., 1999. The big five taxonomy: History, measurement, and theoretical perspectives. In Handbook of Personality Theory and Research, Edited by L. A. Pervin, and O. P. John. Guilford Press: New York, pp. 102-138.
25. Karypis, G., 2003. CLUTO: A clustering toolkit, Technical Report, University of Minnesota, Minneapolis, MN.
26. Kim, E., and, Gilbert, S., and, Edwards, M. J., and, Graeff, E., 2009. Detecting sadness in 140 characters: Sentiment analysis of mourning Michael Jackson on Twitter. In The Web Ecology project, Cambridge, MA, pp. 1-15.
27. Kosinski, M., and, Stillwell, D., and, Graepel, T., 2013. Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110 (15): 5802-5805.
28. Lastovicka, J. L., and, Joachimsthaler, E. A.,. 1988. Improving the detection of personality-behavior relationships in consumer research. Journal of Consumer Research, 14 (4): 583-587.
29. Litman, D. J., and, Forbes-Riley, K., 2004. Predicting student emotions in computer-human tutoring dialogues. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, ACL '04, Barcelona, Spain, pp. 352-359.
30. Liu, H., and, Lieberman, H., and, Selker, T., 2003. A model of textual affect sensing using real-world knowledge. In Proceedings of the 8th International Conference on Intelligent User Interfaces, IUI '03, pp. 125-132.
31. Ma, C., and, Prendinger, H., and, Ishizuka, M., 2005. Emotion estimation and reasoning based on affective textual interaction. In First International Conference on Affective Computing and Intelligent Interaction (ACII-2005), Beijing, China, pp. 622-628.
32. Mairesse, F., and, Walker, M. A., and, Mehl, M. R., and, Moore, R. K., 2007. Using linguistic cues for the automatic recognition of personality in conversation and text. Journal of Artificial Intelligence Research, 30 (1): 457-500.
33. Matykiewicz, P., and, Duch, W., and, Pestian, J., 2009. Clustering semantic spaces of suicide notes and newsgroups articles. In Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing, BioNLP '09, Boulder, CO, pp. 179-184.
34. Mihalcea, R., and, Liu, H., 2006. A corpus-based approach to finding happiness. In AAAI-2006 Spring Symposium on Computational Approaches to Analysing Weblogs, Palo Alto, CA, pp. 139-144.
35. Mohammad, S. M., 2012a. From once upon a time to happily ever after: Tracking emotions in mail and books. Decision Support Systems, 53 (4): 730-741.
36. Mohammad, S. M., 2012b. Portable features for classifying emotional text. In Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2012), Montreal, Canada, pp. 730-741.
37. Mohammad, S. M., and, P. D. Turney,. 2013. Crowdsourcing a word-emotion association lexicon. Computational Intelligence, 29 (3): 436-465.
38. Mohammad, S. M., and, T. Yang,. 2011. Tracking sentiment in mail: How genders differ on emotional axes. In Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011), Portland, OR, 70-79.
39. Myers, I. B., and, McCaulley, M. H., and, R. Most,. 1985. Manual: A Guide to the Development and Use of the Myers-Briggs Type Indicator, Consulting Psychologists Press: Palo Alto, CA.
40. L. B. Myers,. 1962. Manual: The Myers-Briggs Type Indicator, Educational Testing Services: Princeton, NJ.
41. Neviarouskaya, A., and, Prendinger, H., and, M. Ishizuka,. 2009. Compositionality principle in recognition of fine-grained emotions from text. In Proceedings of the Proceedings of the Third International Conference on Weblogs and Social Media (ICWSM-09), San Jose, CA, 278-281.
42. Osgood, C. E., and, Suci, G. J., and, P. Tannenbaum,. 1957. The Measurement of Meaning, University of Illinois Press: Champaign.
43. Osgood, C. E., and, E. G. Walker,. 1959. Motivation and language behavior: A content analysis of suicide notes. Journal of Abnormal and Social Psychology, 59 (1): 58-67.
44. Pearl, L., and, M. Steyvers,. 2010. Identifying emotions, intentions, and attitudes in text using a game with a purpose. In Proceedings of the NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, Los Angeles, CA, pp. 71-79.
45. Pennebaker, J. W., and, L. A. King,. 1999. Linguistic styles: Language use as an individual difference. Journal of Personality and Social Psychology, 77 (6): 1296-1312.
46. Pestian, J. P., P. Matykiewicz, and, J. Grupp-Phelan,. 2008. Using natural language processing to classify suicide notes. In Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing, BioNLP '08, Columbus, OH, pp. 96-97.
47. Platt, J., 1999. Using analytic QP and sparseness to speed training of support vector machines. In Neural Information Processing Systems 11, MIT Press: Cambridge, MA
48. Plutchik, R., 1962. The Emotions. Random House: New York.
49. Plutchik, R., 1980. A general psychoevolutionary theory of emotion. Emotion: Theory, Research, and Experience, 1 (3): 3-33.
50. Plutchik, R., 1985. On emotion: The chicken-and-egg problem revisited. Motivation and Emotion, 9 (2): 197-200.
51. Plutchik, R., 1994. The Psychology and Biology of Emotion. Harper Collins: New York.
52. Qiu, L., H. Lin, J. Ramsay, and, F. Yang,. 2012. You are what you tweet: Personality expression and perception on Twitter. Journal of Research in Personality, 46: 710-718.
53. Ravaja, N., T. Saari, M. Turpeinen, J. Laarni, M. Salminen, and, M. Kivikangas,. 2006. Spatial presence and emotions during video game playing: Does it matter with whom you play? Presence: Teleoperators and Virtual Environments, 15 (4): 381-392.
54. Resnik P,. 1995. Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of the 14th International Joint Conference on Artificial Intelligence-Volume 1, IJCAI'95, Montreal, Canada, pp. 448-453.
55. Strapparava, C., and, R. Mihalcea,. 2007. Semeval-2007 task 14: Affective text. In Proceedings of SemEval-2007, Prague, Czech Republic, pp. 70-74.
56. Strapparava, C., and, A. Valitutti,. 2004. WordNet-affect: An affective extension of WordNet. In Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC-2004), Lisbon, Portugal, pp. 1083-1086.
57. Tett, R. P., D. N. Jackson, and, M. Rothstein,. 1991. Personality measures as predictors of job performance: A meta-analytic review. Personnel Psychology, 44 (4): 703-742.
58. Tumasjan, A., T. O. Sprenger, P. G. Sandner, and, I. M. Welpe,. 2010. Predicting elections with Twitter: What 140 characters reveal about political sentiment. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, Washington, DC, pp. 178-185.
59. Turney, P., and, M. L. Littman,. 2003. Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems, 21 (4): 315-346.
60. Velásquez J. D,. 1997. Modeling emotions and other motivations in synthetic agents. In Proceedings of the Fourteenth National Conference on Artificial Intelligence and Ninth Conference on Innovative Applications of Artificial Intelligence, AAAI'97/IAAI'97, Providence, RI, pp. 10-15.
61. White, J. K., S. S. Hendrick, and, C. Hendrick,. 2004. Big five personality variables and relationship constructs. Personality and Individual Differences, 37 (7): 1519-1530.
62. Yarkoni T.,. 2010. Personality in 100,000 words: A large-scale analysis of personality and word use among bloggers. Journal of Research in Personality, 44 (3): 363-373.
63. Zhe, X., and, A. Boucouvalas,. 2002. Text-to-emotion engine for real time Internet communication. In Proceedings of the International Symposium on CSNDSP, Staffordshire University, Stoke-on-Trent, UK, pp. 164-168.