Abstract
Electronic Participation (eParticipation), both in its traditional form and in its emerging Web 2.0 based form, results in the production of large quantities of textual contributions of citizens concerning government policies and decisions under formation, which contain valuable relevant opinions and knowledge of the society, however are exploited to a limited only extent. It is of critical importance to analyze these contributions in order to extract the opinions and knowledge they contain in a cost-efficient way. This paper reviews a wide range of opinion mining methods, which have been developed for analyzing commercial product opinions and reviews posted on the Web, as to the capabilities they can offer for meeting the above challenges. The review has revealed the great potential of these methods for the analysis of textual citizens’ contributions in public policy debates, both for assessing contributors’ general attitudes-sentiments (positive, negative or neutral) towards the policy/decision under discussion, and also for extracting the main issues they raise (e.g. negative and positive aspects and effects, implementation barriers, improvement suggestions) and the corresponding attitudes-sentiments. Based on the conclusions of this review a basic framework for the use of opinion mining methods in eParticipation has been formulated.
Chapter PDF
Similar content being viewed by others
Keywords
References
Organization for Economic Co-operation & Development (OECD). OECD Hand-book on Information, Consultation and Public Participation in Policy-Making, OECD Publications Service, Paris (2001)
Organization for Economic Co-operation & Development (OECD). Engaging Citizens Online for Better Policy-making - Policy Brief, OECD Publications Service, Paris (2003)
Organization for Economic Co-operation & Development (OECD). Promise and Problems of e-Democracy: Challenges of Online Citizen Engagement, OECD Publications Service, Paris (2004)
Xenakis, A., Loukis, E.: An Investigation of the Use of Structured e-Forum for Enhancing e-Participation in Parliaments. International Journal of Electronic Governance 3(2), 134–147 (2010)
Osimo, D.: Web 2.0 in Government: Why and How? JRC Scientific and Technical Reports. European Commission, Joint Research Centre, Institute for Prospective Technological Studies (2008)
Chadwick, A.: Web 2.0: New Challenges for the Study ofE-Democracy in an Era of Informational Exuberance. I/S: A Journal of Law and Policy for the Information Society 5(1), 9–41 (2009)
Mergel, I.A., Schweik, C.M., Fountain, J.E.: The Transformational Effect of Web 2.0 Technologies on Government (2009), available at SSRN http://www.ssrn.com/abstract=1412796
Charalabidis, Y., Gionis, G., Ferro, E., Loukis, E.: Towards a Systematic Exploitation of Web 2.0 and Simulation Modeling Tools in Public Policy Process. In: Tambouris, E., Macintosh, A., Glassey, O. (eds.) ePart 2010. LNCS, vol. 6229, pp. 1–12. Springer, Heidelberg (2010)
Rose, J., Sanford, C.: Mapping eParticipation Research: Four Central Challenges. Communications of the Association for Information Systems 20, 909–943 (2007)
Macintosh, A., Coleman, S., Schneeberger, A.: eParticipation: The Research Gaps. In: Macintosh, A., Tambouris, E. (eds.) ePart 2009. LNCS, vol. 5694, pp. 1–11. Springer, Heidelberg (2009)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)
Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer, Heidelberg (2006)
Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Language Resources and Evaluation 1(2) (2005)
Godbole, N., Srinivasaiah, M., Skiena, S.: Large-scale sentiment analysis for news and blogs. In: Proceedings of the International Conference on Weblogs and Social Media, ICWSM (2007)
Liu, B., Hu, M., Cheng, J.: Opinion observer: Analyzing and comparing opinions on the web. In: Proceedings of WWW (2005)
Wilson, T., Wiebe, J., Hwa, R.: Just how mad are you? Finding strong and weak opinion clauses. In: Proceedings of AAAI, pp. 761–769 (2004)
Parrott, W.: Emotions in Social Psychology. Psychology Press, Philadelphia (2001)
Aue, A., Gamon, M.: Customizing sentiment classifiers to new domains: A case study. In: Proceedings of Recent Advances in Natural Language Processing, RANLP (2005)
Choi, Y., Breck, E., Cardie, C.: Joint extraction of entities and relations for opinion recognition. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP (2006)
Das, S.R., Chen, M.Y.: Yahoo! for Amazon: Sentiment extraction from small talk on the Web. Management Science 53, 1375–1388 (2007)
Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In: Proceedings of WWW, pp. 519–528 (2003)
Devitt, A., Ahmad, K.: Sentiment analysis in financial news: A cohesion based approach. In: Proceedings of the Association for Computational Linguistics (ACL), pp. 984–991 (2007)
Gamon, M.: Sentiment classification on customer feedback data: Noisy data, large feature vectors, and the role of linguistic analysis. In: Proceedings of the International Conference on Computational Linguistics (COLING) (2004)
Gamon, M., Aue, A., Corston-Oliver, S., Ringger, E.: Pulse: Mining customer opinions from free text. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds.) IDA 2005. LNCS, vol. 3646, pp. 121–132. Springer, Heidelberg (2005)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (2002)
Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the Association for Computational Linguistics (ACL), pp. 417–424 (2002)
Blitzer, J., Dredze, M., Pereira, F.: Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In: Proceedings of the Association for Computational Linguistics, ACL (2007)
Yang, H., Si, L., Callan, J.: Knowledge transfer and opinion detection in the TREC 2006 blog track. In: Proceedings of TREC (2006)
Hatzivassiloglou, V., Wiebe, J.: Effects of adjective orientation and gradability on sentence subjectivity. In: Proceedings of the International Conference on Computational Linguistics, COLING (2000)
Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP (2003)
Nasukawa, T., Yi, J.: Sentiment analysis: Capturing favorability using natural language processing. In: Proceedings of the Conference on Knowledge Capture, K-CAP (2003)
Esuli, A., Sebastiani, F.: SentiWordNet: A publicly available lexical resource for opinion mining. In: Proceedings of Language Resources and Evaluation, LREC (2006)
Riloff, E., Wiebe, J., Wilson, T.: Learning subjective nouns using extraction pattern bootstrapping. In: Proceedings of the Conference on Natural Language Learning (CoNLL), p. 25–32 (2003)
Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP (2003)
Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence 22, 110–125 (2006)
Kim, S.M., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of the International Conference on Computational Linguistics, COLING (2004)
Kim, S.M., Hovy, E.: Automatic identification of pro and con reasons in online reviews. In: Proceedings of the COLING/ACL Main Conference Poster Sessions, pp. 483–490 (2006)
Kim, S.M., Hovy, E.: Crystal: Analyzing predictive opinions on the web. In: Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP/CoNLL (2007)
Kim, S.M., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automatically assessing review helpfulness. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Sydney, Australia, pp. 423–430 (July 2006)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pp. 347–354 (2005)
Polanyi, L., Zaenen, A.: Contextual lexical valence shifters. In: Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text (2004)
Morinaga, S., Yamanishi, K., Tateishi, K., Fukushima, T.: Mining product reputations on the Web. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 341–349 (2002) (Industry Track)
Tong, R.M.: An operational system for detecting and tracking opinions in on-line discussion. In: Proceedings of the Workshop on Operational Text Classification, OTC (2001)
Yi, J., Nasukawa, T., Bunescu, R., Niblack, W.: Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In: Proceedings of the IEEE International Conference on Data Mining, ICDM (2003)
Fellbaum, C. (ed.): Wordnet: An Electronic Lexical Database. MIT Press, Cambridge (1998)
Andreevskaia, A., Bergler, S.: Mining WordNet for a fuzzy sentiment: Sentiment tag extraction from WordNet glosses. In: Proceedings of the European Chapter of the Association for Computational Linguistics, EACL (2006)
Esuli, A., Sebastiani, F.: Determining the semantic orientation of terms through gloss analysis. In: Proceedings of the ACM Conference on Information and Knowledge Management, CIKM (2005)
Esuli, A., Sebastiani, F.: Determining term subjectivity and term orientation for opinion mining. In: Proceedings of the European Chapter of the Association for Computational Linguistics, EACL (2006)
Esuli, A., Sebastiani, F.: PageRankingWordNetsynsets: An application to opinion mining. In: Proceedings of the Association for Computational Linguistics, ACL (2007)
Kamps, J., Marx, M., Mokken, R.J., De Rijke, M.: Using WordNet to measure semantic orientation of adjectives. In: Proc. of LREC 2004, pp. 1115–1118 (2004)
Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the Conference on Web Search and Web Data Mining, WSDM (2008)
Qiu, G., Liu, B., Bu, J., Chen, C.: Expanding Domain Sentiment Lexicon through Double Propagation. In: International Joint Conference on Artificial Intelligence (IJCAI 2009) (2009)
Hatzivassiloglou, V., McKeown, K.: Predicting the semantic orientation of adjectives. In: Proceedings of the Joint ACL/EACL Conference, pp. 174–181 (1997)
Kanayama, H., Nasukawa, T.: Fully automatic lexicon expansion for domain-oriented sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 355–363 (2006)
Popescu, A.M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing, HLT/EMNLP (2005)
Liu, Y., Huang, J., An, A., Yu, X.: ARSA: A sentiment-aware model for predicting sales performance using blogs. In: Proceedings of the ACM Special Interest Group on Information Retrieval, SIGIR (2007)
McDonald, R., Hannan, K., Neylon, T., Wells, M., Reynar, J.: Structured models for fine-to-coarse sentiment analysis. In: Proceedings of the Association for Computational Linguistics (ACL), Prague, Czech Republic, pp. 432–439 (2007)
Su, Q., Xu, X., Guo, H., Wu, X., Zhang, X., Swen, B., Su, Z.: Hidden Sentiment Association in Chinese Web Opinion Mining. In: Proceedings of WWW 2008, pp. 959–968 (2008)
Mei, Q., Ling, X., Wondra, M., Su, H., Zhai, C.X.: Topic sentiment mixture: Modeling facets and opinions in weblogs. In: Proceedings of WWW, pp. 171–180 (2007)
Carenini, G., Ng, R.T., Zwart, E.: Extracting knowledge from evaluative text. In: Proceedings of International Conference on Knowledge Capture (K-CAP), pp. 11–18 (2005)
Qiu, G., Liu, B., Bu, J., Chen, C.: Expanding Domain Sentiment Lexicon through Double Propagation. In: International Joint Conference on Artificial Intelligence, IJCAI 2009 (2009)
I-sieve Technologies, Sentiment Analysis Beyond Impressions, retrieved from http://www.i-sieve.com/
Wei, W., Gulla, J.A.: Sentiment Learning on Product Reviews via Sentiment Ontology Tree. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 404–413. Association for Computational Linguistics, Uppsala (2010)
Riloff, E., Patwardhan, S., Wiebe, J.: Feature subsumption for opinion analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP (2006)
Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 IFIP International Federation for Information Processing
About this paper
Cite this paper
Maragoudakis, M., Loukis, E., Charalabidis, Y. (2011). A Review of Opinion Mining Methods for Analyzing Citizens’ Contributions in Public Policy Debate. In: Tambouris, E., Macintosh, A., de Bruijn, H. (eds) Electronic Participation. ePart 2011. Lecture Notes in Computer Science, vol 6847. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23333-3_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-23333-3_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23332-6
Online ISBN: 978-3-642-23333-3
eBook Packages: Computer ScienceComputer Science (R0)