Abstract

This work proposes a bio-inspired based methodology in order to extract and evaluate user's web texts / posts. To validate the methodology, a dataset is constructed using real data arising from Greek fora. The obtained results are compared with a commonly used machine learning technique (decision trees- C4. 5 algorithm). The bio-inspired algorithm (namely the hybrid PSO/ACO2 algorithm) achieved average classification accuracy 90. 59% in a 10 fold cross validation experiment, outperforming the C4. 5 algorithm (83. 66%). The proposed methodology could be easily integrated with a decision support system providing services in the fields of e-commerce or e-government in order to help merchants acquire customer satisfaction or public administrators capture common understanding.

References

• Kolbitsch, J. and Maurer H. , The transformation of the Web: How emerging communities shape the information we consume. Journal of Universal Computer Science, 2006. 12(2): p. 187-213.
• Seyberd, H. , Internet use in households and by individuals in 2012, t. a. s. Industry, Editor. 2012, Eurostat.
• Xu, G. , Zhang, Y. , Modelling User Behaviour for Web Recommendation Using LDA Model, in in Proceedings of International Workshop on E-Commerce, Business, and Services. 2008.
• Breck, E. and Y. Choi, Joint extraction of entities and relations for opinion recognition, in Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. 2006: Morristown, NJ, USA.
• Ali, H. , Macaulay, L. , Zhao, L. A Collaboration Pattern Language for e-Participation":A Strategy for Reuse. in Proceedings of the 9th European Conference on e-Government. 2009.
• Rushdi Saleh, M. , et al. , Experiments with SVM to classify opinions in different domains. Expert Systems with Applications, 2011. 38(12): p. 14799-14804.
• Dave, K. , Lawrence, S. , Pennock D. M. , Opinion extraction and semantic classi?cation of product reviews, in In Proceedings of WWW. 2003. p. 519-528.
• Chesley, P. , Vincent, B. , Xu, L. , Srihari, R. Using verbs and adjectives to automatically classify blog sentiment. in Proceedings of AAAI-CAAW. 2006.
• Lin, W. H. , Wilson, T. , Wiebe, J. , Hauptmann, A. Which side are you on? Identifying perspectives at the document and sentence levels. in Conference on computational natural language learning. 2006.
• Allen, C. T. , et al. , A place for emotion in attitude models. Journal of Business Research, 2005. 58(4): p. 494-499.
• Shulman, S. , Hovy, E. , Callan, J. , Zavestoski, S. Language processing technologies for electronic rulemaking. in Proceedings of InterNational Conference on Digital Government Research. 2006.
• Hatzivassiloglou, V. , Wiebe, J. , Effects of adjective orientation and gradability on sentence subjectivity, in International Conference on Computational Linguistics. 2000. p. 299-305.
• Kamps, J. , Marx, M. , Mokken, R. J. , Rijke, M. D. , Using wordnet to measure semantic orientation of adjectives. , in Conference on language resources and evaluation. 2004. p. 1115-1118.
• Pang, T. B. , Pang, B. , Lee, L. Thumbs up? sentiment classi?cation using machine learning. in In Proceedings of EMNLP. 2002.
• Mullen, T. , Collier, N. , Sentiment analysis using support vector machines with diverse information sources, in In Proceedings of the conference on empirical methods in natural language processing. 2004. p. 412-418.
• Prabowo, R. , Thelwall, M. , Sentiment analysis: A combined approach. Journal of Informetrics, 2009. 3(2): p. 14.
• Stylios, G. , Katsis, C. D. , Glaros, C. , Simaki, V. , Christodoulakis, D. , MiCoGo, an Integrated System That Automatically Detects the Presence of Opinion in web Texts Regarding eCommerce and eGovernment, in The 12th European Conference on e-Government (ECEG, 2012). 2012: Barcelona, Spain. p. 683-690.
• Holden, N. P. and A. A. Freitas, A hybrid PSO/ACO algorithm for classification, in Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation. 2007, ACM: London, United Kingdom. p. 2745-2750.
• Chomsky, N. , Peng, F. C. C. , Studies on Semantics in Generative Grammar American Anthropologist, 1973. 75(6): p. 1918-1921.
• Carbonell, J. , Subjective Understanding: Computer Models of Belief Systems. PhD thesis, in Yale. 1979.
• Kwon, N. , Shulman, S. W. , Hovy, E, Multidimensional text analysis for eRulemaking, in Proceedings of DG. O (Inter)National Conference on Digital Government Research. 2006.
• Shulman, S. , Hovy, E. , Callan, J. , Zavestoski, S. Language processing technologies for electronic rulemaking. in Proceedings of DG. O (Inter)National Conference on Digital Government Research. 2006.
• O'Leary, D. E. , Blog mining-review and extensions: "From each according to his opinion". Decision Support Systems, 2011. 51(4): p. 821-830.
• Pang, B. , Lee Li. Opinion Mining and Sentiment Analysis. Foundations and Trends in information Retrieval, 2008. 2, 1-135 DOI: DOI:10. 1561/1500000001.
• Esuli, K. , Opinion mining. 2009.
• Ku, L. W. and H. H. Chen, Mining opinions from the web: Beyond relevance retrieval. Journal of the American Society for Information Science and Technology, 2007. 58(12): p. 1838-1850.
• Xu, Z. and R. Ramnath. Mining opinion from Poll Results in Web Pages in www2009. 2009. Madrid, Spain.
• Furuse, O. , et al. Opinion sentence search engine on open-domain blog. in Proccedings of the 20th International Joint Conference on Artificial Intelligence in Hyderabad. 2007. India.
• Quan, X. J. , et al. , Short text similarity based on probabilistic topics. Knowledge and Information Systems, 2010. 25(3): p. 473-491.
• Weng, L. , et al. , Query by document via a decomposition-based two-level retrieval approach, in Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. 2011, ACM: Beijing, China. p. 505-514.
• Nallapati, R. , Cohen, W. , A New Unsupervised Model for Topics and Influence of Blogs, in In International Conference for Weblogs and Social Media. 2008. p. 84-92.
• Xiaowen, D. , Liu, B. The utility of linguistic rules in opinion mining. in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval 2007. New York.
• Miao, Q. L. , Q. D. Li, and R. W. Dai, AMAZING: A sentiment mining and retrieval system. Expert Systems with Applications, 2009. 36(3): p. 7192-7198.
• Zhongwu. Zhai, et al. Clustering Product Features for Opinion Mining. in Proceedings of 4th ACM International Conference on Web Search and Data Minin, . 2011. Hong Kong, China.
• Sun, J. T. , et al. A comparative web search system. in 15th International Conference on World Wide Web (WWW). 2006. Edinburg, Scotland.
• Breck, E. , Y. Choi, and C. Cardie. Identifying expressions of opinion in context. in In Proceedings of the 20th International Joint Conference on Arti?cial Intelligence (IJCAI-2007). 2007. Hyderabad, India,.
• Breck, E. , Y. Choi, and C. Cardie. Joint extraction of entities and relations for opinion recognition. in Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. 2006. Morristown, NJ, USA.
• Choi, Y. and C. Cardie. Structured local training and biased potential functions for conditional random ?elds with application to coreference resolution. in In Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; . 2007. Rochester, New York.
• Ulincy, B. , et al. Uses of Ontologies in Open-Source Blog Mining in In proceedings of the Ontology for the intelligence Community. 2007. Columbia, Maryland.
• Jin, X. , Zhou, Y. , Mobasher, B. A unified approach to personalization based on probabilistic latent semantic models of web usage and content. in AAAI 2004 Workshop on Semantic Web Personalization (SWP'04). 2004. San Jose.
• Wiebe, J. Learning subjective adjectives from corpora. in In Proceedings of the 17th National Conference on Artificial Intelligence (AAAI -2000). 2000.
• Wiebe, J. , et al. , Learning subjective language. 2002, University of Pittsburgh: Pennsylvania. .
• Hatzivassiloglou, V. and J. M. Wiebe, Effects of adjective orientation and gradability on sentence subjectivity, in Proceedings of the 18th conference on Computational linguistics - Volume 1. 2000, Association for Computational Linguistics: Saarbrücken, Germany. p. 299-305.
• Roussinov, D. and J. L. Zhao, Automatic discovery of similarity relationships through Web mining. Decision Support Systems, 2003. 35(1): p. 149-166.
• Wong, T. -L. and W. Lam, An unsupervised method for joint information extraction and feature mining across different Web sites. Data & Knowledge Engineering, 2009. 68(1): p. 107-125.
• Yang, H. -C. and C. -H. Lee, A text mining approach on automatic generation of web directories and hierarchies. Expert Systems with Applications, 2004. 27(4): p. 645-663.
• Velásquez, J. D. , L. E. Dujovne, and G. L'Huillier, Extracting significant Website Key Objects: A Semantic Web mining approach. Engineering Applications of Artificial Intelligence, 2011. 24(8): p. 1532-1541.
• Turney, P. Thumps up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. in In Proceedings of the 40th Annual Meeting of the association for Computational linguistics. 2002.
• Pang, B. , L. Lee, and S. Vaithyanathan. Thumps up? Sentiment classification using machine learning techniques. in In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP-02). 2002.
• Yu, H. and V. Hatzivassiloglou. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. in In Proceedings of the Conference on empirical Methods in Natural language Processing 2003.
• Stylios, G. , et al. Deciphering the Public Stance Towards Govermental Decisions. in 10th European Conference on E-Government. Ireland.
• Wiebe, J. , R. Bruce, and T. O'Hara. Development and use of a gold standard data set for subjectivy classifications. . in In Proceedings of the 37th Annual Meeting of the Association for Computational Lingyistics (ACL-99). 1999.
• Stylios G. , et al. , Public Opinion Mining for Governmental Decisions. Online Journal for Government. , 2011.
• Eneva, E. , Hoberman, R. , Lita, L. , Learning Within-Sentence Semantic Coherence, in Empirical Methods in Natural Language Processing. 2001.
• Lapata, M. , Barzilay, R. , Automatic evaluation of text coherence: Models and representations, in Proceedings of the 19th International Joint Conference on Artificial Intelligence. 2005.
• Barzilay, R. and M. Lapata, Modeling local coherence: An entity-based approach. Computational Linguistics, 2008. 34(1): p. 1-34.
• Holden, N. and A. A. Freitas, A Hybrid PSO/ACO Algorithm for Discovering Classification Rules in Data Mining. Journal of Artificial Evolution and Applications, 2008. 2008.
• Quinlan, R. J. , Programs for Machine Learning. 1993: Morgan Kauffman.
• Kennedy, J. and R. Eberhart. Particle swarm optimization. in Neural Networks, 1995. Proceedings. , IEEE International Conference on. 1995.
• Dorigo, M. , V. Maniezzo, and A. Colorni, Ant system: Optimization by a colony of cooperating agents. Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics, 1996. 26(1): p. 29-41.
• Geisser, S. , Predictive Inference (Chapman & Hall/CRC Monographs on Statistics & Applied Probability). 1993: Published by Chapman and Hall/CRC
• Kohavi, R. , "A study of cross-validation and bootstrap for accuracy estimation and model selection", in Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. 1995, Morgan Kaufmann. p. 1137-1143.
• Devijver, P. A. , Pattern Recognition: A Statistical Approach. 1982, London, GB: Prentice-Hall.
• Stylios, G. , Christodoulakis, D. , Besharat, J. , Vonitsanou, M. , Kotrotsos, I. , Koumpouri, A. Stamou, S, Public Opinion Mining for Governmental Decisions. Electronic Journal of e-Governmen, 2010. 8(2): p. 14.