Abstract
In Natural Language Processing or Computational Linguistics (NLP or CL), researchers assume almost universally that speakers hold some affective value or sentiment with regard to (some aspects of) a topic such as a film or camera, that this sentiment has a fixed value (typically, something like good or bad), and that the sentiment is expressed in text through a word or small combination of words. However, one finds in the NLP literature essentially no discussion about what ‘sentiment’ or ‘opinion’ really is, how it is expressed in actual language usage, how the expressing words are organized and found in the lexicon, and how in fact one can empirically verify cognitive claims, if any, implied in or assumed by an NLP implementation. Even the Wikipedia definition, which is a little more careful than most of the NLP literature, uses words like “polarity”, “affective state”, and “emotional effect” without definition. In this situation we can usefully try to duplicate Michael’s mindset and approach. What do people actually do? How does what they do illustrate the complexities of the problem and disclose unusual and interesting aspects that computer scientists are simply blind to? In this paper I first provide interesting examples of real-world usage, then explore some definitions of sentiment, affect, opinion, and emotion, and conclude with a few suggestions for how computational studies might address the problem in a more informed way. I hope in the paper to follow the spirit of Michael’s research, in recognizing that there is much more to language usage than simply making some computer system mimic some annotated corpus, and that one can learn valuable lessons for NLP by looking at what people do when they make language.
Keywords
Dedicated to Michael Zock.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agarwal, A., & Mittal, N. (2013). Optimal feature selection methods for sentiment analysis. Proceedings of the 14th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing) (Vol. 7817, pp. 13–24).
Balahur, A., & Tanev, H. (2012). Detecting entity-related events and sentiments from tweets using multilingual resources. Proceedings of the CLEF Workshop Online Working Notes.
Gryc, W., & Moilanenm, K. (2010). Leveraging textual sentiment analysis with social network modelling: sentiment analysis of political blogs in the 2008 U.S. Presidential Election. Proceedings of the workshop `From Text to Political Positions’ Amsterdam: Vrije Universiteit.
Kim, S.-M., & Hovy, E. H. (2005). Automatic detection of opinion bearing words and sentences. Companion Volume to the Proceedings of the International Joint Conference on Natural Language Processing (IJCNLP) (pp. 61–66).
Kim, S.-M., & Hovy, E. H. (2006). Identifying and analyzing judgment opinions. Proceedings of the Human Language Technology/North American Association of Computational Linguistics conference (HLT-NAACL 2006).
Ogneva, M. (2012). How companies can use sentiment analysis to improve their business. From http://mashable.com/2010/04/19/sentiment-analysis/.
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? sentiment classification using machine learning techniques. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 79–86).
Riloff, E., & Wiebe, J. (2003). Learning extraction patterns for subjective expressions. Proceedings of the conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 105–112).
Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for twitter sentiment analysis. Proceedings of the Workshop on Emotion and Sentiment Analysis (ESSEM) at the AI*IA Conference.
Snyder, B., & Barzilay, R. (2007). Multiple Aspect Ranking using the good grief algorithm. Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL) (pp. 300–307).
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C., Ng, A., et al. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2013).
Steinberger, J., Lenkova, P., & Kabadjov, M., Steinberger, R., & van der Goot, E. (2011). Multilingual entity-centered sentiment analysis evaluated by parallel corpora. Proceedings of the Conference on Recent Advancements in Natural Language Processing (RANLP).
Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., & Li, P. (2011). User-level sentiment analysis incorporating social networks. Proceedings of the KDD conference.
Turney P. (2002). Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. Proceedings of the Association for Computational Linguistics (pp.417–424). arXiv:cs.LG/0212032.
Wang, F., Wu, Y., & Qiu, L. (2012a). Exploiting discourse relations for sentiment analysis. Proceedings of COLING conference posters (pp. 1311–1320).
Wang, J., Yu, C. T., Yu, P. S., Liu, B., & Meng, W. (2012b). Diversionary comments under political blog posts. Proceedings of the 21st ACM international conference on Information and knowledge management (CIKM).
Wiebe, J., Wilson, T., & Cardie, C. (2005). Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, 39(2–3), 165–210.
Yu, H., & Hatzivassiloglou, V. (2003). Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. Proceedings of the conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 129–136).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Hovy, E.H. (2015). What are Sentiment, Affect, and Emotion? Applying the Methodology of Michael Zock to Sentiment Analysis. In: Gala, N., Rapp, R., Bel-Enguix, G. (eds) Language Production, Cognition, and the Lexicon. Text, Speech and Language Technology, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-319-08043-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-08043-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08042-0
Online ISBN: 978-3-319-08043-7
eBook Packages: Computer ScienceComputer Science (R0)