Abstract
Two approaches to generating domain-specific sentiment lexicons are proposed: (i) direct generation and (ii) adaptation. The first is founded on the idea of generating a dedicated lexicon directly from labelled source data. The second approach is founded on the idea of using an existing general purpose lexicon and adapting this so that it becomes a specialised lexicon with respect to some domain. The operation of the two approaches is illustrated using a political opinion mining domain and evaluated using a large corpus of labelled political speeches extracted from political debates held within the UK Houses of Commons.
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.
Similar content being viewed by others
References
Asmi, A., Ishaya, T.: A framework for automated corpus generation for semantic sentiment analysis. In: Proc. World Congress on Engineering (WCE 2012), pp. 436–444 (2012)
Grijzenhout, S., Jijkoun, V., Marx, M.: Opinion mining in dutch hansards. In: Proceedings of the Workshop From Text to Political Positions. Free University of Amsterdam (2010)
Esuli, A., Sebastiani, F.: SentiWordNet: A publicly available lexical resource for opinion mining. In: Proceedings from the International Conference on Language Resources and Evaluation, LREC (2006)
Denecke, K.: Are sentiwordnet scores suited for multi-domain sentiment classification? In: Fourth International Conference on Digital Information Management, ICDIM 2009, pp. 1–6 (2009)
Montejo-Raez, A., Martínez-Cámara, E., Martin-Valdivia, M., Ureña-López, L.: Random walk weighting over sentiwordnet for sentiment polarity detection on twitter. In: Proc 3rd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, pp. 3–10 (2012)
Ohana, B., Tierney, B.: Sentiment classification of reviews using sentiwordnet. In: Proceedings of the 9th IT & T Conference, Dublin Institute of Technology (2009)
Salah, Z., Coenen, F., Grossi, D.: Extracting debate graphs from parliamentary transcripts: A study directed at UK House of Commons debates. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law (ICAIL 2013), Rome, Italy, pp. 121–130 (2013)
Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010). European Language Resources Association, ELRA (2010)
Thelwall, M., Buckley, K.: Topic-based sentiment analysis for the social web: The role of mood and issue-related words. Journal of the American Society for Information Science and Technology 64(8), 1608–1617 (2013)
Birla, V.K., Gautam, R., Shukla, V.: Retrieval and creation of domain specific lexicon from noisy text data. In: Proceedings of ASCNT-2011, CDAC, Noida, Indiaz (2011)
Demiroz, G., Yanikoglu, B., Tapucu, D., Saygin, Y.: Learning domain-specific polarity lexicons. In: 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW), pp. 674–679 (2012)
Choi, Y., Cardie, C.: Adapting a polarity lexicon using integer linear programming for domainspecific sentiment classification. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp. 590–598 (2009)
Weichselbraun, A., Gindl, S., Scharl, A.: Using games with a purpose and bootstrapping to create domain-specific sentiment lexicons. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, pp. 1053–1060. ACM, New York (2011)
Qiu, G., Liu, B., Bu, J., Chen, C.: Expanding domain sentiment lexicon through double propagation. In: Proceedings of the 21st International Jont Conference on Artifical Intelligence, IJCAI 2009, pp. 1199–1204. Morgan Kaufmann Publishers Inc., San Francisco (2009)
Lau, R., Zhang, W., Bruza, P., Wong, K.: Learning domain-specific sentiment lexicons for predicting product sales. In: 2011 IEEE 8th International Conference on e-Business Engineering (ICEBE), pp. 131–138 (2011)
Ringsquandl, M., Petković, D.: Expanding opinion lexicon with domain specific opinion words using semi-supervised approach. In: BRACIS 2012 - WTI, IV International Workshop on Web and Text Intelligence (2012)
Zhang, J., Peng, Q.: Constructing chinese domain lexicon with improved entropy formula for sentiment analysis. In: 2012 International Conference on Information and Automation (ICIA), pp. 850–855 (2012)
Wilks, Y., Stevenson, M.: The grammar of sense: Using part-of-speech tags as a first step in semantic disambiguation. Natural Language Engineering 4(5), 135–143 (1998)
Kuhn, A., Ducasse, S., Gibra, T.: Semantic clustering: Identifying topics in source code. Information and Software Technology 49(3), 230–243 (2007)
Li, H., Sun, C., Wan, K.: Clustering web search results using conceptual grouping. In: Proc. 8th International Conference on Machine Learning and Cybernetics, pp. 12–15 (2009)
Martineau, J., Finin, T.: Delta tfidf: An improved feature space for sentiment analysis. In: Proc 3rd International ICWSM Conference, pp. 258–261 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Salah, Z., Coenen, F., Grossi, D. (2013). Generating Domain-Specific Sentiment Lexicons for Opinion Mining. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53914-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-53914-5_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-53913-8
Online ISBN: 978-3-642-53914-5
eBook Packages: Computer ScienceComputer Science (R0)