Abstract
Opinion target extraction is a crucial task of opinion mining, aiming to extract occurrences of the different entities of a corpus that are subjects of an opinion. In order to produce a readable and comprehensible opinion summary, which is the main application of opinion target extraction, these occurrences are consolidated at the entity level in a second task. In this paper we argue that combining the two tasks, i.e. extracting opinion targets using entities as labels instead of binary labels, yields better results for opinion target extraction. We compare the binary approach and the multi-class approach on available datasets in English and French, and conduct several investigation experiments to explain the promising results. Our experiment show that an entity-based labelling not only improves opinion extraction in a single domain setting, but also let us combine training data from different domains to improve the extraction, a result that has never been achieved on target-based training data.
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
References
Jakob, N., Gurevych, I.: Extracting opinion targets in a single- and cross-domain setting with conditional random fields. In: Proceedings of EMNLP 2010, Cambridge, MA, USA, pp. 1035–1045 (2010)
Liu, B.: Sentiment analysis and opinion mining. In: Synthesis Lectures on Human Language Technologies. Springer, Boston (2012)
Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: Semeval-2014 task 4: aspect based sentiment analysis. In: Proceedings of SemEval 2014, Dublin, Ireland (2014)
Li, F., Pan, S., Jin, O., Yang, Q., Zhu, X.: Cross-domain co-extraction of sentiment and topic lexicons. In: Proceedings of ACL 2010, Uppsala, Sweden, pp. 410–419 (2012)
Hamilton, W.L., Clark, K., Leskovec, J., Jurafsky, D.: Inducing domain-specific sentiment lexicons from unlabeled corpora (2016). arXiv preprint arXiv:1606.02820
Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of ACL 1997, Madrid, Spain, pp. 174–181 (1997)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of KDD 2004, Seattle, WA, USA, pp. 68–177 (2004)
Kim, S.M., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of COLING 2004, Geneva, Switzerland, p. 1367 (2004)
Bethard, S., Yu, H., Thornton, A., Hatzivassiloglou, V., Jurafsky, D.: Automatic extraction of opinion propositions and their holders. In: Proceedings of AAAI 2004, Palo Alto, CA, USA (2004)
Popescu, A.M., Etzioni, O.: Extracting product features and opinion from reviews. In: Proceedings of HLT-EMNLP 2005, Vancouver, Canada, pp. 339–346 (2005)
Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of WWW 2005, Chiba, Japan, pp. 342–351 (2005)
Kim, S.M., Hovy, E.: Extracting opinions, opinion holders, and topics expressed in online news media text. In: Proceedings of SST 2006, Sydney, Australia (2006)
Mukherjee, A., Liu, B.: Aspect extraction through semi-supervised modeling. In: Proceedings of ACL 2012, Jeju Island, Korea, pp. 339–348 (2012)
Kobayashi, N., Inui, K., Matsumoto, Y.: Opinion mining from web documents: extraction and structurization. Trans. Jpn. Soc. Artif. Intell. 22, 227–238 (2007)
Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of WSDM 2008, Palo Alto, CA, USA, pp. 231–240 (2008)
Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: aspect based sentiment analysis. In: Proceedings of SemEval 2015, Denver, CO, USA, pp. 486–495 (2015)
Pontiki, M., et al.: Semeval-2016 task 5: aspect based sentiment analysis, San Diego, CA, USA (2016)
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of ICML 2001, San Francisco, CA, USA, pp. 282–289 (2001)
Breck, E., Choi, Y., Cardie, C.: Identifying expressions of opinion in context. IJCAI 7, 2683–2688 (2007)
Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: Proceedings of SIGIR 1994, Dublin, Ireland, pp. 3–12 (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Lark, J., Morin, E., Peña Saldarriaga, S. (2018). A Comparative Study of Target-Based and Entity-Based Opinion Extraction. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-77116-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77115-1
Online ISBN: 978-3-319-77116-8
eBook Packages: Computer ScienceComputer Science (R0)