Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text

Emotion Measurement

Volumn , Issue , 2016, Pages 201-237

  Mohammad, Saif M ^a  

^a NATIONAL RESEARCH COUNCIL CANADA   (Canada)

Author keywords

Emotion detection; Machine learning; Natural language processing; Sentiment analysis; Sentiment lexicons; Stance detection; Subjectivity

Indexed keywords

EID: 84978710849     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/B978-0-08-100508-8.00009-6     Document Type: Chapter

References (215)

1. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. (2011). Sentiment analysis of twitter data. In Proceedings of language in social media (pp. 30-38), Portland, OR.
2. Aisopos, F., Papadakis, G., Tserpes, K., & Varvarigou, T. (2012). Textual and contextual patterns for sentiment analysis over microblogs. In Proceedings of the 21st WWW companion (pp. 453-454), New York, NY.
3. Alm C.O. Affect in text and speech 2008, Ann Arbor, MI, USA: ProQuest.
4. Alm, C.O., Roth, D., & Sproat, R. (2005). Emotions from text: Machine learning for text-based emotion prediction. In Proceedings of the joint conference on HLT-EMNLP, Vancouver, Canada.
5. Aman, S., & Szpakowicz, S. (2007). Identifying expressions of emotion in text. In Text, speech and dialogue, Vol. 4629 of lecture notes in computer science (pp. 196-205).
6. Anagnostopoulos C.-N., Iliou T., Giannoukos I. Features and classifiers for emotion recognition from speech: A survey from 2000 to 2011. Artificial Intelligence Review 2015, 43(2):155-177.
7. Anand, P., Walker, M., Abbott, R., Tree, J. E. F., Bowmani, R., & Minor, M. (2011). Cats rule and dogs drool!: Classifying stance in online debate. In Proceedings of the 2nd workshop on computational approaches to subjectivity and sentiment analysis (pp. 1-9).
8. Avello, D. G. (2012). "i wanted to predict elections with twitter and all i got was this lousy paper"-a balanced survey on election prediction using twitter data. arXiv, 1204.6441.
9. Baker, C. F., Fillmore, C. J., & Lowe, J. B. (1998). The Berkeley framenet project. In Proceedings of ACL (pp. 86-90), Stroudsburg, PA.
10. Bakliwal, A., Arora, P., Madhappan, S., Kapre, N., Singh, M., & Varma, V. (2012). Mining sentiments from tweets. In Proceedings of WASSA'12 (pp. 11-18), Jeju, Republic of Korea.
11. Balahur A., Turchi M. Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis. Computer Speech & Language 2014, 28(1):56-75.
12. Barrett L.F. Are emotions natural kinds?. Perspectives on Psychological Science 2006, 1(1):28-58.
13. Bellegarda, J. (2010). Emotion analysis using latent affective folding and embedding. In Proceedings of the NAACL-HLT 2010 workshop on computational approaches to analysis and generation of emotion in text, Los Angeles, CA.
14. Bermingham A., Smeaton A.F. On using twitter to monitor political sentiment and predict election results. Psychology 2011, 2-10.
15. Bock, R., Gluge, S., Wendemuth, A., Limbrecht, K., Walter, S., Hrabal, D., et al. (2012). Intraindividual and interindividual multimodal emotion analyses in human-machine interaction. In Cognitive methods in situation awareness and decision support (CogSIMA), 2012 IEEE international multi-disciplinary conference (pp. 59-64). IEEE.
16. Bollen, J., Pepe, A., & Mao, H. (2009). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. CoRR.
17. Boucouvalas A.C. Real time text-to-emotion engine for expressive internet communication. Emerging Communication: Studies on New Technologies and Practices in Communication 2002, 5:305-318.
18. Bradley, M. M., & Lang, P. J. (1999). Affective norms for english words (anew): Instruction manual and affective ratings. Tech. rep., Technical Report C-1, The Center for Research in Psychophysiology, University of Florida.
19. Bravo-Marquez, F., Frank, E., & Pfahringer, B. (2015). Positive, negative, or neutral: Learning an expanded opinion lexicon from emoticon-annotated tweets.
20. Brody, S., & Diakopoulos, N. (2011). Cooooooooooooooollllllllllllll!!!!!!!!!!!!!!: using word lengthening to detect sentiment in microblogs. In Proceedings of the conference on empirical methods in natural language processing, EMNLP'11 (pp. 562-570).
21. Brooks, M., Kuksenok, K., Torkildson, M. K., Perry, D., Robinson, J. J., Scott, T. J., et al. (2013). Statistical affect detection in collaborative chat. In Proceedings of the 2013 conference on Computer supported cooperative work (pp. 317-328). ACM.
22. Carvalho, P., Sarmento, L., Silva, M. J., & De Oliveira, E. (2009). Clues for detecting irony in user-generated contents: oh.!! it's so easy;-). In Proceedings of the 1st international CIKM workshop on topic-sentiment analysis for mass opinion (pp. 53-56). ACM.
23. Chaffar, S., & Inkpen, D. (2011). Using a heterogeneous dataset for emotion analysis in text. In Canadian Conference on AI (pp. 62-67).
24. Chen, Y., Zhou, Y., Zhu, S., & Xu, H. (2012). Detecting offensive language in social media to protect adolescent online safety. In Privacy, security, risk and trust (PASSAT), 2012 international conference and 2012 international conference on social computing (SocialCom) (pp. 71-80). IEEE.
25. Cherry C., Mohammad S.M., De Bruijn B. Binary classifiers and latent sequence models for emotion detection in suicide notes. Biomedical Informatics Insights 2012, 5(Suppl. 1):147.
26. Chetviorkin, I., Moscow, L. G., & Loukachevitch, N. (2014). Two-step model for sentiment lexicon extraction from twitter streams. In Proceedings of the workshop on computational approaches to subjectivity and sentiment analysis (pp. 67-72), Baltimore, MD.
27. Choi, Y., & Cardie, C. (2008). Learning with compositional semantics as structural inference for subsentential sentiment analysis. In Proceedings of the conference on empirical methods in natural language processing, EMNLP'08 (pp. 793-801), Honolulu, HI.
28. Choudhury, M. D., Counts, S., & Gamon, M. (2012). Not all moods are created equal! exploring human emotional states in social media. In The International AAAI Conference on Weblogs and Social Media (ICWSM).
29. Collobert R., Weston J., Bottou L., Karlen M., Kavukcuoglu K., Kuksa P. Natural language processing (almost) from scratch. The Journal of Machine Learning Research 2011, 12:2493-2537.
30. Conover, M. D., Goncalves, B., Ratkiewicz, J., Flammini, A., & Menczer, F. (2011). Predicting the political alignment of Twitter users. In IEEE third international conference on privacy security risk and trust and IEEE third international conference on social computing (pp. 192-199). IEEE.
31. Conover M.D., Ratkiewicz J., Francisco M., Gonc B., Flammini A., Menczer F. Political polarization on Twitter. Networks 2011, 133(26):89-96.
32. Dadvar M., Trieschnigg D., Ordelman R., de Jong F. Improving cyberbullying detection with user context. Advances in information retrieval 2013, 693-696. Springer.
33. Das, A., & Bandyopadhyay, S. (2009). Subjectivity detection in english and bengali: A crf-based approach. In Proceeding of ICON.
34. Davis, H., & Mohammad, S. (2014). Generating music from literature. In Proceedings of the 3rd workshop on computational linguistics for literature (CLFL) (pp. 1-10), Gothenburg, Sweden.
35. De Leersnyder, J., Boiger, M., & Mesquita, B. (2015). Cultural differences in emotions. Emerging trends in the social and behavioral sciences: An interdisciplinary, searchable, and linkable resource.
36. Dogan H. Emotion, confidence, perception and expectation case of mathematics. International Journal of Science and Mathematics Education 2012, 10(1):49-69.
37. Eichstaedt J.C., Schwartz H.A., Kern M.L., Park G., Labarthe D.R., Merchant R.M., et al. Psychological language on twitter predicts county-level heart disease mortality. Psychological Science 2015, 26(2):159-169.
38. Ekman P. An argument for basic emotions. Cognition & Emotion 1992, 6(3):169-200.
39. Ekman P., Friesen W.V. Unmasking the face: A guide to recognizing emotions from facial clues 2003, Ishk.
40. El Ayadi M., Kamel M.S., Karray F. Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern Recognition 2011, 44(3):572-587.
41. Esuli, A., & Sebastiani, F. (2006). SENTIWORDNET: A publicly available lexical resource for opinion mining. In Proceedings of the 5th conference on language resources and evaluation, LREC'06 (pp. 417-422).
42. Fang, R. -Y., Chen, B. -W., Wang, J. -F., & Wu, C. -H. (2011). Emotion detection based on concept inference and spoken sentence analysis for customer service. In Twelfth annual conference of the international speech communication association.
43. Filatova, E. (2012). Irony and sarcasm: Corpus generation and analysis using crowdsourcing. In LREC (pp. 392-398).
44. Fillmore, C. (1982). Frame semantics. Linguistics in the morning calm (pp. 111-137).
45. Fillmore, C. J. (1976). Frame semantics and the nature of language. In Annals of the New York Academy of Sciences: Conference on the Origin and Development of Language and Speech, Vol. 280 (pp. 20-32).
46. Francisco V., Gervás P. Automated mark up of affective information in english texts. Text, speech and dialogue, Vol. 4188 of lecture notes in computer science 2006, 375-382. Springer, Berlin/Heidelberg. P. Sojka, I. Kopecek, K. Pala (Eds.).
47. Frijda N.H. The laws of emotion. American Psychologist 1988, 43(5):349.
48. Genereux, M., & Evans, R. P. (2006). Distinguishing affective states in weblogs. In AAAI2006 spring symposium on computational approaches to analysing weblogs (pp. 27-29), Stanford, CA.
49. Ghazi, D., Inkpen, D., & Szpakowicz, S. (2015). Detecting emotion stimuli in emotion-bearing sentences. In Proceedings of the 2015 conference on intelligent text processing and computational linguistics.
50. Gobron S., Ahn J., Paltoglou G., Thelwall M., Thalmann D. From sentence to emotion: A real-time three-dimensional graphics metaphor of emotions extracted from text. The Visual Computer 2010, 26(6-8):505-519.
51. Golbeck, J., & Hansen, D. (2011). Computing political preference among twitter followers. In Proceedings of the SIGCHI conference on human factors in computing systems, CHI'11 (pp. 1105-1108). New York: ACM.
52. González-Ibánez, R., Muresan, S., & Wacholder, N. (2011). Identifying sarcasm in twitter: a closer look. In Proceedings of the ACL (pp. 581-586). Association for Computational Linguistics.
53. Grijalva, E., Newman, D. A., Tay, L., Donnellan, M. B., Harms, P., Robins, R. W., et al. (2014). Gender differences in narcissism: A meta-analytic review.
54. Gupta N., Gilbert M., Fabbrizio G.D. Emotion detection in email customer care. Computational Intelligence 2013, 29(3):489-505.
55. Hartner M. The lingering after-effects in the readers mind-an investigation into the affective dimension of literary reading. Journal of Literary Theory Online 2013.
56. Hasan, K. S., & Ng, V. (2013). Stance classification of ideological debates: Data, models, features, and constraints. In Proceedings of the 6th international joint conference on natural language processing (pp. 1348-1356).
57. Hasan, M., Rundensteiner, E., & Agu, E. (2014). Emotex: Detecting emotions in twitter messages.
58. Hatzivassiloglou, V., & McKeown, K. R. (1997). Predicting the semantic orientation of adjectives. In Proceedings of the 8th conference of european chapter of the association for computational linguistics (pp. 174-181), Madrid, Spain.
59. Hatzivassiloglou, V., & Wiebe, J. M. (2000). Effects of adjective orientation and gradability on sentence subjectivity. In Proceedings of the 18th conference on computational linguistics-Volume 1 (pp. 299-305). Association for Computational Linguistics.
60. Hill, F., & Korhonen, A. (2014). Learning abstract concept embeddings from multi-modal data: Since you probably cant see what i mean. In Proceedings of EMNLP. ACL.
61. Holzman, L. E., & Pottenger, W. M. (2003). Classification of emotions in internet chat: An application of machine learning using speech phonemes. Tech. rep., Leigh University.
62. Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, KDD'04 (pp. 168-177). New York, NY: ACM.
63. Irsoy, O., & Cardie, C. (2014). Deep recursive neural networks for compositionality in language. Advances in neural information processing systems, pp. 2096-2104.
64. Jia, L., Yu, C., & Meng, W. (2009). The effect of negation on sentiment analysis and retrieval effectiveness. In Proceedings of the 18th ACM conference on information and knowledge management, CIKM'09 (pp. 1827-1830). New York, NY: ACM.
65. John, D., Boucouvalas, A. C., & Xu, Z. (2006). Representing emotional momentum within expressive internet communication. In Proceedings of the 24th IASTED internationalconference on Internet and multimedia systems and applications (pp. 183-188). Anaheim, CA: ACTA Press.
66. Johnsen J.-A.K., Vambheim S.M., Wynn R., Wangberg S.C. Language of motivation and emotion in an internet support group for smoking cessation: explorative use of automated content analysis to measure regulatory focus. Psychology Research and Behavior Management 2014, 7:19.
67. Jurgens, D., Mohammad, S. M., Turney, P., & Holyoak, K. (2012). Semeval-2012 task 2: Measuring degrees of relational similarity. In Proceedings of the 6th international workshop on semantic evaluation, SemEval'12 (pp. 356-364), Montréal, Canada.
68. Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014). A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188.
69. Karpathy, A., Joulin, A., & Li, F. F. F. (2014). Deep fragment embeddings for bidirectional image sentence mapping. In Advances in neural information processing systems, pp. 1889-1897.
70. Kennedy, A., & Inkpen, D. (2005). Sentiment classification of movie and product reviews using contextual valence shifters. In Proceedings of the workshop on the analysis of informal and formal information exchange during negotiations, Ottawa, ON, Canada.
71. Kessler, J. S., & Nicolov, N. (2009). Targeting sentiment expressions through supervised ranking of linguistic configurations. In 3rd Int'l AAAI conference on weblogs and social media (ICWSM'09), San Jose, CA.
72. Kiela, D., & Bottou, L. (2014). Learning image embeddings using convolutional neural networks for improved multi-modal semantics. In Proceedings of EMNLP, Vol. 2014.
73. Kilgarriff A. I dont believe in word senses. Computers and the Humanities 1997, 31(2):91-113.
74. Kim, E., Gilbert, S., Edwards, M. J., & Graeff, E. (2009). Detecting sadness in 140 characters: Sentiment analysis of mourning Michael Jackson on twitter.
75. Kirange D., et al. Emotion classification of news headlines using svm. Asian Journal of Computer Science & Information Technology 2013, 2(5).
76. Kiritchenko, S., Zhu, X., Cherry, C., & Mohammad, S. M. (2014). NRC-Canada-2014: Detecting aspects and sentiment in customer reviews. In Proceedings of the international workshop on semantic evaluation, SemEval'14, Dublin, Ireland.
77. Kiritchenko S., Zhu X., Mohammad S.M. Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research 2014, 50:723-762.
78. Kiros, R., Salakhutdinov, R., & Zemel, R. S. (2014). Unifying visual-semantic embeddings with multimodal neural language models. arXiv preprint arXiv:1411.2539.
79. Kleres J. Emotions and narrative analysis: A methodological approach. Journal for the Theory of Social Behaviour 2011, 41(2):182-202.
80. Kramer, A. D. (2012). The spread of emotion via facebook. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 767-770). ACM.
81. Ku, L. -W., Liang, Y. -T., & Chen, H. -H. (2006). Opinion extraction, summarization and tracking in news and blog corpora. In AAAI spring symposium: Computational approaches to analyzing weblogs, Vol. 100107.
82. Kunneman, F., Liebrecht, C., & van den Bosch, A. (2014). The (un) predictability of emotional hashtags in twitter. In Proceedings of the 5th workshop on language analysis for social media (pp. 26-34), Gothenburg, Sweden.
83. Lampos, V., Preotiuc-Pietro, D., & Cohn, T. (2013). A user-centric model of voting intention from social media. In Proceedings of the 51st annual meeting of the association for computational linguistics (pp. 993-1003).
84. Lapponi E., Read J., Ovrelid L. Representing and resolving negation for sentiment analysis. ICDM workshops 2012, 687-692. IEEE Computer Society. J. Vreeken, C. Ling, M.J. Zaki, A. Siebes, J.X. Yu, B. Goethals, G.I. Webb, X. Wu (Eds.).
85. Lassen D.S., Brown A.R. Twitter the electoral connection?. Social Science Computer Review 2011, 29(4):419-436.
86. Lazaridou, A., Bruni, E., & Baroni, M. (2014). Is this a wampimuk? cross-modal mapping between distributional semantics and the visual world. In Proceedings of ACL (pp. 1403-1414).
87. Le, Q. V., & Mikolov, T. (2014). Distributed representations of sentences and documents. arXiv preprint arXiv:1405.4053.
88. Li, J., Zhou, G., Wang, H., & Zhu, Q. (2010). Learning the scope of negation via shallow semantic parsing. In Proceedings of the 23rd international conference on computational linguistics, COLING'10 (pp. 671-679), Beijing, China.
89. Li X.G., Li S.M., Jiang L.R., Zhang S.B. Study of english pronunciation quality evaluation system with tone and emotion analysis capabilities. Applied Mechanics and Materials 2014, 475:318-323.
90. Lin, C., He, Y., & Everson, R. (2011). Sentence subjectivity detection with weakly-supervised learning. In IJCNLP (pp. 1153-1161).
91. Lindquist K.A., Wager T.D., Kober H., Bliss-Moreau E., Barrett L.F. The brain basis of emotion: a meta-analytic review. Behavioral and Brain Sciences 2012, 35(03):121-143.
92. Liu B. Sentiment analysis: Mining opinions, sentiments, and emotions 2015, Cambridge University Press.
93. Liu B., Zhang L. A survey of opinion mining and sentiment analysis. Mining text data 2012, 415-463. Springer, USA. C.C. Aggarwal, C. Zhai (Eds.).
94. Liu, H., Lieberman, H., & 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). New York, NY: ACM.
95. Liu H., Selker T., Lieberman H. Visualizing the affective structure of a text document. CHI'03 extended abstracts on human factors in computing systems 2003, 740-741. ACM.
96. Liu, J., Cao, Y., Lin, C. -Y., Huang, Y., & Zhou, M. (2007). Low-quality product review detection in opinion summarization. In EMNLP-CoNLL (pp. 334-342).
97. Lloret, E., Balahur, A., Palomar, M., & Montoyo, A. (2009). Towards building a competitive opinion summarization system: Challenges and keys. In Proceedings of human language technologies: The 2009 annual conference of the North American chapter of the association for computational linguistics, companion volume: Student research workshop and doctoral consortium (pp. 72-77). Association for Computational Linguistics.
98. Louviere, J.J. (1991). Best-worst scaling: A model for the largest difference judgments. Working Paper.
99. Lu, B., & Tsou, B. K. (2010). Cityu-dac: Disambiguating sentiment-ambiguous adjectives within context. In Proceedings of the 5th international workshop on semantic evaluation (pp. 292-295). Association for Computational Linguistics.
100. Ma, C., Prendinger, H., & Ishizuka, M. (2005). Emotion estimation and reasoning based on affective textual interaction. In J. Tao, & R. W. Picard (Eds.), First international conference on affective computing and intelligent interaction (ACII-2005) (pp. 622-628), Beijing, China.
101. Makki, R., Brooks, S., & Milios, E. E. (2014). Context-specific sentiment lexicon expansion via minimal user interaction. In Proceedings of the international conference on information visualization theory and applications, pp. 178-186, Rome, Italy.
102. Malti T., Krettenauer T. The relation of moral emotion attributions to prosocial and antisocial behavior: A meta-analysis. Child Development 2013, 84(2):397-412.
103. Martínez-Cámara, E., Martín-Valdivia, M. T., Urenalópez, L. A., & Montejoráez, A. R. (2012). Sentiment analysis in Twitter. Natural Language Engineering, 1-28.
104. Maynard D., Funk A. Automatic detection of political opinions in tweets. gateacuk 2011, 7117:81-92.
105. McNair, D., Lorr, M., & Droppleman, L. (1989). Profile of mood states (poms).
106. Mihalcea, R., Banea, C., & Wiebe, J. (2007). Learning multilingual subjective language via cross-lingual projections. In Proceedings of the 45th annual meeting of the association of computational linguistics.
107. Mihalcea, R., & Liu, H. (2006). A corpus-based approach to finding happiness. In AAAI 2006 spring symposium on computational approaches to analysing weblogs (pp. 139-144). AAAI Press.
108. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111-3119).
109. Minamikawa A., Yokoyama H. Personality estimation based on weblog text classification. Modern approaches in applied intelligence 2011, 89-97. Springer.
110. Mohammad, S. (2011). From once upon a time to happily ever after: Tracking emotions in novels and fairy tales. In Proceedings of the 5th ACL-HLT workshop on language technology for cultural heritage, social sciences, and humanities (pp. 105-114). Portland, OR: Association for Computational Linguistics.
111. Mohammad, S. (2012). Portable features for classifying emotional text. In Proceedings of the 2012 conference of the North American chapter of the association for computational linguistics: Human language technologies (pp. 587-591), Montréal, Canada.
112. Mohammad, S. M. (2012a). #emotional tweets. In Proceedings of the first joint conference on lexical and computational semantics-Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the 6th international workshop on semantic evaluation, SemEval'12 (pp. 246-255), Stroudsburg, PA.
113. Mohammad S.M. From once upon a time to happily ever after: Tracking emotions in mail and books. Decision Support Systems 2012, 53(4):730-741.
114. Mohammad, S. M., & Kiritchenko, S. (2013). Using nuances of emotion to identify personality. In Proceedings of the international conference on weblogs and social media (ICWSM-13), Boston, MA.
115. Mohammad, S.M., & Kiritchenko, S. (2014). Using hashtags to capture fine emotion categories from tweets. To appear in Computational Intelligence.
116. Mohammad, S. M., & Turney, P. D. (2010). Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon. In Proceedings of the NAACLHLT 2010 workshop on computational approaches to analysis and generation of emotion in text, Los Angeles, CA.
117. Mohammad, S. M., & Turney, P. D. (2012). Crowdsourcing a word-emotion association lexicon. Computational Intelligence.
118. Mohammad, S. M., Dunne, C., & Dorr, B. (2009). Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus. In Proceedings of the conference on empirical methods in natural language processing: Volume 2, EMNLP'09 (pp. 599-608).
119. Mohammad, S. M., Salameh, M., & Kiritchenko, S. (2015). How translation alters sentiment. Journal of Artificial Intelligence Research.
120. Mohammad, S. M., Zhu, X., Kiritchenko, S., & Martin, J. (2014). Sentiment, emotion, purpose, and style in electoral tweets. Information Processing and Management.
121. Mohammad, S. M., Zhu, X., Kiritchenko, S., & Martin, J. (2015). Sentiment, emotion, purpose, and style in electoral tweets. Information Processing and Management.
122. Mohammad, S., & Yang, T. (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'11) (pp. 70-79). Portland, OR: Association for Computational Linguistics.
123. Mohammad, S., Kiritchenko, S., & Zhu, X. (2013a). NRC-Canada: Building the state-ofthe-art in sentiment analysis of tweets. In Proceedings of the international workshop on semantic evaluation, SemEval'13, Atlanta, GA.
124. Mohammad, S., Kiritchenko, S., & Zhu, X. (2013b). NRC-Canada: Building the state-ofthe-art in sentiment analysis of tweets. In Proceedings of the seventh international workshop on Semantic Evaluation Exercises (SemEval-2013), Atlanta, GA.
125. Montero C.S., Munezero M., Kakkonen T. Investigating the role of emotion-based features in author gender classification of text. Computational linguistics and intelligent text processing 2014, 98-114. Springer.
126. Morante R., Sporleder C. Modality and negation: An introduction to the special issue. Computational Linguistics 2012, 38(2):223-260.
127. Murakami, A., & Raymond, R. (2010). Support or oppose? Classifying positions in online debates from reply activities and opinion expressions. In Coling 2010: Posters (pp. 869-875). Beijing, China: Coling 2010 Organizing Committee.
128. Nalisnick, E. T., & Baird, H. S. (2013a). Character-to-character sentiment analysis in Shakespeares plays. In Document analysis and recognition (ICDAR), 2013 12th international conference. IEEE.
129. Nalisnick, E. T., & Baird, H. S. (2013b). Extracting sentiment networks from shakespeare's plays. In Document analysis and recognition (ICDAR), 2013 12th international conference (pp. 758-762). IEEE.
130. Navigli R. Word sense disambiguation: A survey. ACM Computing Surveys (CSUR) 2009, 41(2):10.
131. Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (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) (pp. 278-281), San Jose, CA.
132. Nielsen, F. A. (2011). Afinn.
133. Orme B. Maxdiff analysis: Simple counting, individual-level logit, and HB 2009, Sawtooth Software, Inc.
134. Ortony A., Clore G.L., Collins A. The cognitive structure of emotions 1988, Cambridge University Press.
135. Osgood C.E., Suci G.J., Tannenbaum P. The measurement of meaning 1957, University of Illinois Press.
136. Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of the 7th conference on international language resources and evaluation, LREC'10, Valletta, Malta.
137. Pang B., Lee L. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2008, 2(1-2):1-135.
138. Parrot W. Emotions in social psychology 2001, Psychology Press.
139. Paul, M. J., & Dredze, M. (2011). You are what you tweet: Analyzing twitter for public health. In ICWSM (pp. 265-272).
140. Pearl, L., & Steyvers, M. (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.
141. Pennebaker J.W., Mehl M.R., Niederhoffer K.G. Psychological aspects of natural language use: Our words, our selves. Annual Review of Psychology 2003, 54(1):547-577.
142. Picard R.W. Affective computing. Affective computing 1997 2000, MIT Press, MIT Press.
143. Pit M. Determining subjectivity in text: The case of backward causal connectives in dutch. Discourse Processes 2006, 41(2):151-174.
144. Plutchik R. The emotions 1962, Random House, New York, NY.
145. Plutchik R. A general psychoevolutionary theory of emotion. Emotion: Theory, Research, and Experience 1980, 1(3):3-33.
146. Plutchik R. The emotions 1991, University Press of America.
147. Plutchik R. The nature of emotions human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. American Scientist 2001, 89(4):344-350.
148. Polanyi, L., & Zaenen, A. (2004). Contextual valence shifters. In Exploring attitude and affect in text: Theories and applications (AAAI Spring Symposium Series).
149. Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., & Manandhar, S. (2014). SemEval-2014 Task 4: Aspect based sentiment analysis. In Proceedings of the international workshop on semantic evaluation, SemEval'14, Dublin, Ireland.
150. Popescu, A. -M., & Etzioni, O. (2005). Extracting product features and opinions from reviews. In Proceedings of the conference on human language technology and empirical methods in natural language processing, HLT'05 (pp. 339-346), Stroudsburg, PA.
151. Purver, M., & Battersby, S. (2012). Experimenting with distant supervision for emotion classification. In Proceedings of the 13th conference of the European chapter of the association for computational linguistics, EACL'12 (pp. 482-491), Stroudsburg, PA.
152. Qadir, A. (2009). Detecting opinion sentences specific to product features in customer reviews using typed dependency relations. In Proceedings of the workshop on events in emerging text types, eETTs'09 (pp. 38-43), Stroudsburg, PA.
153. Quan, C., & Ren, F. (2009). Construction of a blog emotion corpus for chinese emotional expression analysis. In Proceedings of the 2009 conference on empirical methods in natural language processing: Volume 3 (pp. 1446-1454). Association for Computational Linguistics.
154. Quan C., Ren F. Visualizing emotions from chinese blogs by textual emotion analysis and recognition techniques. International Journal of Information Technology & Decision Making 2014, 1-20.
155. Ren F., Quan C. Linguistic-based emotion analysis and recognition for measuring consumer satisfaction: An application of affective computing. Information Technology and Management 2012, 13(4):321-332.
156. Reyes A., Rosso P., Veale T. A multidimensional approach for detecting irony in twitter. Language Resources and Evaluation 2013, 47(1):239-268.
157. Riloff, E., & Wiebe, J. (2003). Learning extraction patterns for subjective expressions. In Proceedings of the 2003 conference on empirical methods in natural language processing (pp. 105-112). Association for Computational Linguistics.
158. Rosenthal, S., Nakov, P., Kiritchenko, S., Mohammad, S. M., Ritter, A., & Stoyanov, V. (2015). Semeval-2015 task 10: Sentiment analysis in twitter. In Proceedings of the 9th international workshop on semantic evaluation, SemEval.
159. Rosenthal, S., Nakov, P., Ritter, A., & Stoyanov, V. (2014). SemEval-2014 Task 9: Sentiment Analysis in Twitter. In P. Nakov & T. Zesch (Eds.), Proceedings of the 8th international workshop on semantic evaluation, SemEval-2014, Dublin, Ireland.
160. Rude S., Gortner E.-M., Pennebaker J. Language use of depressed and depression-vulnerable college students. Cognition & Emotion 2004, 18(8):1121-1133.
161. Russell J.A. A circumplex model of affect. Journal of Personality and Social Psychology 1980, 39(6):1161.
162. Salameh, M., Mohammad, S. M., & Kiritchenko, S. (2015). Sentiment after translation: A case-study on arabic social media posts. In Proceedings of the North American chapter of association of computational linguistics, Denver, CO.
163. Schwartz, H., Eichstaedt, J., Kern, M., Dziurzynski, L., Lucas, R., Agrawal, M., et al. (2013). Characterizing geographic variation in well-being using tweets. In Proceedings of the international AAAI conference on weblogs and social media.
164. Shaver P., Schwartz J., Kirson D., O'connor C. Emotion knowledge: Further exploration of a prototype approach. Journal of Personality and Social Psychology 1987, 52(6):1061.
165. Socher, R., Perelygin, A., Wu, J. Y., Chuang, J., Manning, C. D., Ng, A. Y., et al. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the conference on empirical methods in natural language processing, EMNLP'13, Seattle, USA.
166. Somasundaran, S., & Wiebe, J. (2009). Recognizing stances in online debates. In Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP: Volume 1, ACL'09 (pp. 226-234). Stroudsburg, PA: Association for Computational Linguistics.
167. Somprasertsri G., Lalitrojwong P. Mining feature-opinion in online customer reviews for opinion summarization. Journal of UCS 2010, 16(6):938-955.
168. Sridhar, D., Getoor, L., & Walker, M. (2014). Collective stance classification of posts in online debate forums. ACL 2014, 109.
169. Stone P., Dunphy D.C., Smith M.S., Ogilvie D.M., associates The general inquirer: A computer approach to content analysis 1966, The MIT Press.
170. Stoyanov, V., & Cardie, C. (2006). Toward opinion summarization: Linking the sources. In Proceedings of the workshop on sentiment and subjectivity in text (pp. 9-14). Association for Computational Linguistics.
171. Strapparava, C., & Mihalcea, R. (2007). Semeval-2007 task 14: Affective text. In Proceedings of SemEval-2007 (pp. 70-74), Prague, Czech Republic.
172. Su, F., & Markert, K. (2008). From words to senses: a case study of subjectivity recognition. In Proceedings of the 22nd international conference on computational Linguistics-Volume 1 (pp. 825-832). Association for Computational Linguistics.
173. Su, Q., Xiang, K., Wang, H., Sun, B., & Yu, S. (2006). Using pointwise mutual information to identify implicit features in customer reviews. In Proceedings of the 21st international conference on computer processing of oriental languages: Beyond the orient: the research challenges ahead, ICCPOL'06 (pp. 22-30). Berlin, Heidelberg: Springer-Verlag.
174. Suero Montero, C., & Suhonen, J. (2014). Emotion analysis meets learning analytics: Online learner profiling beyond numerical data. In Proceedings of the 14th koli calling international conference on computing education research (pp. 165-169). ACM.
175. Sun Y., Quan C., Kang X., Zhang Z., Ren F. Customer emotion detection by emotion expression analysis on adverbs. Information Technology and Management 2014, 1-9.
176. Suttles J., Ide N. Distant supervision for emotion classification with discrete binary values. Computational Linguistics and Intelligent Text Processing 2013, 121-136. Springer.
177. Taboada M., Brooke J., Tofiloski M., Voll K., Stede M. Lexicon-based methods for sentiment analysis. Computational Linguistics 2011, 37(2):267-307.
178. Taboada, M., Voll, K., & Brooke, J. (2008). Extracting sentiment as a function of discourse structure and topicality. Simon Fraser Univeristy School of Computing Science Technical Report.
179. Tang, D., Wei, F., Qin, B., Liu, T., & Zhou, M. (2014). Coooolll: A deep learning system for twitter sentiment classification. In Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014) (pp. 208-212).
180. Tang, D., Wei, F., Qin, B., Zhou, M., & Liu, T. (2014). Building large-scale twitter-specific sentiment lexicon: A representation learning approach. In Proceedings of COLING (pp. 172-182).
181. Thelwall M., Buckley K., Paltoglou G. Sentiment in Twitter events. Journal of the American Society for Information Science and Technology 2011, 62(2):406-418.
182. Thelwall M., Buckley K., Paltoglou G., Cai D., Kappas A. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology 2010, 61(12):2544-2558.
183. Thomas, B., et al. (2014). Synthesized feature space for multiclass emotion classification. In Networks & soft computing (ICNSC), 2014 first international conference (pp. 188-192). IEEE.
184. Thomas, M., Pang, B., & Lee, L. (2006). Get out the vote: Determining support or opposition from congressional floor-debate transcripts. In Proceedings of the 2006 conference on empirical methods in natural language processing (pp. 327-335), Sydney, Australia.
185. Tokuhisa, R., Inui, K., & Matsumoto, Y. (2008). Emotion classification using massive examples extracted from the web. In Proceedings of the 22nd international conference on computational linguistics-Volume 1, COLING'08 (pp. 881-888).
186. Tumasjan A., Sprenger T.O., Sandner P.G., Welpe I.M. Election forecasts with twitter: How 140 characters reflect the political landscape. Social Science Computer Review 2010, 29(4):402-418.
187. Tumasjan A., Sprenger T.O., Sandner P.G., Welpe I.M. Predicting elections with Twitter: What 140 characters reveal about political sentiment. Word Journal of The International Linguistic Association 2010, 178-185.
188. Turney P., Littman M.L. Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems 2003, 21(4).
189. Veale, T., & Hao, Y. (2010). Detecting ironic intent in creative comparisons. In ECAI, Vol. 215, pp. 765-770.
190. Vo B., Collier N. Twitter emotion analysis in earthquake situations. International Journal of Computational Linguistics and Applications 2013, 4(1):159-173.
191. Walker, M. A., Anand, P., Abbott, R., & Grant, R. (2012). Stance classification using dialogic properties of persuasion. In Proceedings of the 2012 conference of the North American chapter of the association for computational linguistics: Human language technologies (pp. 592-596).
192. Wan, X. (2008). Using bilingual knowledge and ensemble techniques for unsupervised chinese sentiment analysis. In Proceedings of the conference on empirical methods in natural language processing, EMNLP'08 (pp. 553-561).
193. Wang, C., & Wang, F. (2012). A bootstrapping method for extracting sentiment words using degree adverb patterns. In Computer Science & Service System (CSSS), 2012 international conference (pp. 2173-2176). IEEE.
194. Wang, W., Chen, L., Thirunarayan, K., & Sheth, A. P. (2012). Harnessing twitter "big data" for automatic emotion identification. In Proceedings of the 2012 ASE/IEEE international conference on social computing and 2012 ASE/IEEE international conference on privacy, security, risk and trust, SOCIALCOM-PASSAT'12 (pp. 587-592). Washington, DC: IEEE Computer Society.
195. Wang, X., & Fu, G. -H. (2010). Chinese subjectivity detection using a sentiment density-based naive bayesian classifier. In Machine Learning and Cybernetics (ICMLC), 2010 international conference, Vol. 6, pp. 3299-3304. IEEE.
196. Wang, Z. (2014). Segment-based fine-grained emotion detection for chinese text. CLP 2014, 52.
197. Warriner A.B., Kuperman V., Brysbaert M. Norms of valence, arousal, and dominance for 13,915 english lemmas. Behavior Research Methods 2013, 45(4):1191-1207.
198. Wen, S., & Wan, X. (2014). Emotion classification in microblog texts using class sequential rules. In Twenty-eighth AAAI conference on Artificial intelligence.
199. Wiebe J., Riloff E. Creating subjective and objective sentence classifiers from unannotated texts. Computational linguistics and intelligent text processing 2005, 486-497. Springer.
200. Wiebe J., Riloff E. Finding mutual benefit between subjectivity analysis and information extraction. Affective Computing, IEEE Transactions 2011, 2(4):175-191.
201. Wiebe J., Wilson T., Bruce R., Bell M., Martin M. Learning subjective language. Computational Linguistics 2004, 30(3):277-308.
202. Wiegand, M., Balahur, A., Roth, B., Klakow, D., & Montoyo, A. (2010). A survey on the role of negation in sentiment analysis. In Proceedings of the workshop on negation and speculation in natural language processing, NeSp-NLP'10 (pp. 60-68), Stroudsburg, PA.
203. Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., et al. (2005). Opinionfinder: A system for subjectivity analysis. In Proceedings of hlt/emnlp on interactive demonstrations (pp. 34-35). Association for Computational Linguistics.
204. Wilson, T., Kozareva, Z., Nakov, P., Rosenthal, S., Stoyanov, V., & Ritter, A. (2013). SemEval-2013 Task 2: Sentiment analysis in Twitter. In Proceedings of the international workshop on semantic evaluation, SemEval'13, Atlanta, GA.
205. Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the conference on human language technology and empirical methods in natural language processing, HLT'05 (pp. 347-354), Stroudsburg, PA.
206. Xu, G., Huang, C. -R., & Wang, H. (2013). Extracting chinese product features: representing a sequence by a set of skip-bigrams. In Proceedings of the 13th Chinese conference on Chinese Lexical Semantics, CLSW'12 (pp. 72-83). Berlin, Heidelberg: Springer-Verlag.
207. Xu, R., Wong, K. -F., Lu, Q., Xia, Y., & Li, W. (2008). Learning knowledge from relevant webpage for opinion analysis. In Web intelligence and intelligent agent technology, 2008, WI-IAT'08, IEEE/WIC/ACM international conference on, Vol. 1, pp. 307-313. IEEE.
208. Yen, H. Y., Lin, P. H., & Lin, R. (2014). Emotional product design and perceived brand emotion.
209. Yu L.-C., Wu J.-L., Chang P.-C., Chu H.-S. Using a contextual entropy model to expand emotion words and their intensity for the sentiment classification of stock market news. Knowledge-Based Systems 2013, 41:89-97.
210. Zhang C., Zeng D., Xu Q., Xin X., Mao W., Wang F.-Y. Polarity classification of public health opinions in chinese. Intelligence and security informatics 2008, 449-454. Springer.
211. Zhang, L., Liu, B., Lim, S. H., & O'Brien-Strain, E. (2010). Extracting and ranking product features in opinion documents. In Proceedings of the 23rd international conference on computational linguistics: Posters, COLING'10 (pp. 1462-1470), Stroudsburg, PA.
212. Zhe, X., & Boucouvalas, A. (2002). Text-to-emotion engine for real time internet Communication text-to-emotion engine for real time internet communication (pp. 164-168).
213. Zhu, X., Guo, H., Mohammad, S., & Kiritchenko, S. (2014). An empirical study on the effect of negation words on sentiment. In Proceedings of the 52nd annual meeting of the association for computational linguistics (Volume 1: Long Papers) (pp. 304-313), Baltimore, MD.
214. Zhu, X., Kiritchenko, S., & Mohammad, S. M. (2014). NRC-Canada-2014: Recent improvements in sentiment analysis of tweets. In Proceedings of the international workshop on semantic evaluation, SemEval'14, Dublin, Ireland.
215. Zhu, X., Sobhani, P., & Guo, H. (2015). Long short-term memory over recursive structures. In Proceedings of international conference on machine learning.