Abstract
Expressions of emotion abound in user-generated content, whether it be in blogs, reviews, or on social media. Much work has been devoted to detecting and classifying these emotions, but little of it has acknowledged the fact that emotionally charged text may express multiple emotions at the same time. We describe a new dataset of user-generated movie reviews annotated for emotional expressions, and experimentally validate two algorithms that can detect multiple emotions in each sentence of these reviews.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alm, C.O., Roth, D., Sproat, R.: Emotions from text: machine learning for text-based emotion prediction. In: Proc. HLT–EMNLP, pp. 579–586 (2005)
Aman, S., Szpakowicz, S.: Identifying expressions of emotion in text. In: Matoušek, V., Mautner, P. (eds.) TSD 2007. LNCS (LNAI), vol. 4629, pp. 196–205. Springer, Heidelberg (2007)
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Müller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., Vanderplas, J., Joly, A., Holt, B., Varoquaux, G.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop on Languages for Machine Learning (2013)
Calvo, R.A., D’Mello, S.K.: Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Trans. on Affective Computing 1(1), 18–37 (2010)
Danisman, T., Alpkocak, A.: Feeler: emotion classification of text using vector space model. In: Proc. AISB Convention (2008)
D’Mello, S.K., Craig, S.D., Sullins, J., Graesser, A.C.: Predicting affective states expressed through an emote-aloud procedure from AutoTutor’s mixed-initiative dialogue. Int’l J. AI in Education 16, 3–28 (2006)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: A library for large linear classification. JMLR 9, 1871–1874 (2008)
Godbole, S., Sarawagi, S.: Discriminative methods for multi-labeled classification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 22–30. Springer, Heidelberg (2004)
Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity. In: Proc. ACL (2004)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. JMLR 12 (2011)
Sechidis, K., Tsoumakas, G., Vlahavas, I.: On the stratification of multi-label data. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 145–158. Springer, Heidelberg (2011)
Shaver, P., Schwartz, J., Kirson, D., O’Connor, C.: Emotion knowledge: further exploration of a prototype approach. J. Personality and Social Psychology 52(6) (1987)
Stenetorp, P., Pyysalo, S., Topić, G., Ohta, T., Ananiadou, S., Tsujii, J.: BRAT: a web-based tool for NLP-assisted text annotation. In: Demos at 13th Conf. EACL, pp. 102–107 (2012)
Strapparava, C., Mihalcea, R.: SemEval-2007 task 14: Affective text. In: Proc. 4th Int’l Workshop on Semantic Evaluations, pp. 70–74 (2007)
Strapparava, C., Mihalcea, R.: Learning to identify emotions in text. In: Proc. ACM Symp. Applied Computing, pp. 1556–1560 (2008)
Tan, E.: Emotion and the structure of narrative film. Erlbaum, Mahwah (1996)
Trohidis, K., Tsoumakas, G., Kalliris, G., Vlahavas, I.: Multi-label classification of music into emotions. In: Proc. Int’l Conf. on Music IR, pp. 325–330 (2008)
Tsoumakas, G., Katakis, I.: Multi-label classification: An overview. Int’l J. Data Warehousing and Mining 3(3), 1–13 (2007)
Tsoumakas, G., Vlahavas, I.: Random k-labelsets: An ensemble method for multilabel classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 406–417. Springer, Heidelberg (2007)
Yang, C., Lin, K.H.Y., Chen, H.H.: Emotion classification using web blog corpora. In: IEEE/WIC/ACM Int’l Conf. on Web Intelligence, pp. 275–278 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Buitinck, L., van Amerongen, J., Tan, E., de Rijke, M. (2015). Multi-emotion Detection in User-Generated Reviews. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-16354-3_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16353-6
Online ISBN: 978-3-319-16354-3
eBook Packages: Computer ScienceComputer Science (R0)