Abstract
The Internet is frequently used as a medium for exchange of information and opinions, as well as propaganda dissemination. In this study the use of sentiment analysis methodologies is proposed for classification of Web forum opinions in multiple languages. The utility of stylistic and syntactic features is evaluated for sentiment classification of English and Arabic content. Specific feature extraction components are integrated to account for the linguistic characteristics of Arabic. The entropy weighted genetic algorithm (EWGA) is also developed, which is a hybridized genetic algorithm that incorporates the information-gain heuristic for feature selection. EWGA is designed to improve performance and get a better assessment of key features. The proposed features and techniques are evaluated on a benchmark movie review dataset and U.S. and Middle Eastern Web forum postings. The experimental results using EWGA with SVM indicate high performance levels, with accuracies of over 91% on the benchmark dataset as well as the U.S. and Middle Eastern forums. Stylistic features significantly enhanced performance across all testbeds while EWGA also outperformed other feature selection methods, indicating the utility of these features and techniques for document-level classification of sentiments.
- Abbasi, A. and Chen, H. 2005. Identification and comparison of extremist-group Web forum messages using authorship analysis. IEEE Intell. Syst. 20, 5, 67--75. Google ScholarDigital Library
- Abbasi, A. and Chen, H. 2006. Visualizing authorship for identification. In Proceedings of the 4th IEEE International Conference on Intelligence and Security Informatics, San Diego, CA, 60--71. Google ScholarDigital Library
- Abbasi, A. and Chen, H. 2007a. Affect intensity analysis of Dark Web forums. In Proceedings of the 5th IEEE International Conference on Intelligence and Security Informatics, New Brunswick, NJ, 282--288.Google Scholar
- Abbasi, A. and Chen, H. 2007b. Analysis of affect intensities in extremist group forums. In Intelligence and Security Informatics. E. Reid and H. Chen, Eds. Springer (forthcoming).Google Scholar
- Alexouda, G. and Papparrizos, K. 2001. A genetic algorithm approach to the product line design problem using the seller's return criterion: An extensive comparative computational study. Eur. J. Oper. Res. 134, 165--178.Google ScholarCross Ref
- Aggarwal, C. C., Orlin, J., and Tai, R. P. 1997. Optimized crossover for the independent set problem. Oper. Res. 45, 2, 226--234.Google ScholarDigital Library
- Agrawal, R., Rajagopalan, S., Srikant, R., and Xu, Y. 2003. Mining newsgroups using networks arising from social behavior. In Proceedings of the 12th International World Wide Web Conference (WWW), 529--535. Google ScholarDigital Library
- Balakrishnan, P. V., Gupta, R., and Jacob, V. S. 2004. Development of hybrid genetic algorithms for product line designs. IEEE Trans. Syst. Man Cybernet. 34, 1, 468--483. Google ScholarDigital Library
- Beineke, P., Hastie, T., and Vaithyanathan, S. 2004. The sentimental factor: Improving review classification via human-provided information. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, 263. Google ScholarDigital Library
- Burris, V., Smith, E., and Strahm, A. 2000. White supremacist networks on the Internet. Sociol. Focus 33, 2, 215--235.Google ScholarCross Ref
- Chen, A. and Gey, F. 2002. Building an Arabic stemmer for information retrieval. In Proceedings of the 11th Text Retrieval Conference (TREC), Gaithersburg, MD, 631--639.Google Scholar
- Chen, H. 2006. Intelligence and Security Informatics for International Security: Information Sharing and Data Mining. Springer, London. Google ScholarDigital Library
- Crilley, K. 2001. Information warfare: New battle fields, terrorists, propaganda, and the Internet. Aslib Proc. 53, 7, 250--264.Google ScholarCross Ref
- Dash, M. and Liu, H. 1997. Feature selection for classification. Intell. Data Anal. 1, 131--156.Google ScholarCross Ref
- Dave, K., Lawrence, S., and Pennock, D. M. 2003. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th International Conference on the World Wide Web (WWW), 519--528. Google ScholarDigital Library
- De Vel, O., Anderson, A., Corney, M., and Mohay, G. 2001. Mining e-mail content for author identification forensics. ACM SIGMOD Rec. 30, 4, 55--64. Google ScholarDigital Library
- Donath, J. 1999. Identity and deception in the virtual community. In Communities in Cyberspace, Routledge Press, London.Google Scholar
- Efron, M. 2004. Cultural orientations: Classifying subjective documents by cocitation analysis. In Proceedings of the AAAI Fall Symposium Series on Style and Meaning in Language, Art, Music, and Design, 41--48.Google Scholar
- Efron, M., Marchionini, G., and Zhiang, J. 2004. Implications of the recursive representation problem for automatic concept identification in on-line government information. In Proceedings of the Annual Meeting of the American Society for Information Science and Technology (ASIST) SIG-CR Workshop.Google Scholar
- Fei, Z., Liu, J., and Wu, G. 2004. Sentiment classification using phrase patterns. In Proceedings of the 4th IEEE International Conference on Computer Information Technology, 1147--1152. Google ScholarDigital Library
- Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3, 1289--1305. Google ScholarCross Ref
- Gamon, M. 2004. Sentiment classification on customer feedback data: Noisy data, large feature vectors, and the role of linguistic analysis. In Proceedings of the 20th International Conference on Computational Linguistics, 841. Google ScholarDigital Library
- Glaser, J., Dixit, J., and Green, D. P. 2002. Studying hate crime with the Internet: What makes racists advocate racial violence? J. Social Issues 58, 1, 177--193.Google ScholarCross Ref
- Grefenstette, G., Qu, Y., Shanahan, J. G., and Evans, D. A. 2004. Coupling niche browsers and affect analysis for an opinion mining application. In Proceedings of the 12th International Conference Recherche d'Information Assistee par Ordinateur, 186--194.Google Scholar
- Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. 2002. Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389--422. Google ScholarDigital Library
- Guyon, I. and Elisseeff, A. 2003. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157--1182. Google ScholarDigital Library
- Hatzivassiloglou, V. and McKeown, K. R. 1997. Predicting the semantic orientation of adjectives. In Proceedings of the 35th Annual Meeting of the Association of Computational Linguistics, 174--181. Google ScholarDigital Library
- Hearst, M. A. 1992. Direction-Based text interpretation as an information access refinement. In Text-Based Intelligent Systems: Current Research and Practice in Information Extraction and Retrieval, P. Jacobs, Ed. Lawrence Erlbaum Associates, Mahwah, NJ. Google ScholarDigital Library
- Henley, N. M., Miller, M. D., Beazley, J. A., Nguyen, D. N., Kaminsky, D., and Sanders, R. 2002. Frequency and specificity of referents to violence in news reports of anti-gay attacks. Discourse Soc. 13, 1, 75--104.Google ScholarCross Ref
- Herring, S., Job-Sluder, K., Scheckler, R., and Barab, S. 2002. Searching for safety online: Managing “trolling” in a feminist forum. The Inf. Soc. 18, 5, 371--384.Google ScholarCross Ref
- Herring, S. and Paolillo, J. C. 2006. Gender and genre variations in Weblogs. J. Sociolinguist. 10, 4, 439.Google ScholarCross Ref
- Holland, J. 1975. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI. Google ScholarDigital Library
- Hu, M. and Liu, B. 2004. Mining and summarizing customer reviews. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 168--177. Google ScholarDigital Library
- Jain, A. and Zongker, D. 1997. Feature selection: Evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Mach. Intell. 19, 2, 153--158. Google ScholarDigital Library
- Jiang, M., Jensen, E., Beitzel, S. and Argamon, S. 2004. Choosing the right bigrams for information retrieval. In Proceedings of the Meeting of the International Federation of Classification Societies.Google Scholar
- Juola, P. and Baayen, H. 2005. A controlled-corpus experiment in authorship identification by cross-entropy. Literar. Linguist. Comput. 20, 59--67.Google ScholarCross Ref
- Kanayama, H., Nasukawa, T., and Watanabe, H. 2004. Deeper sentiment analysis using machine translation technology. In Proceedings of the 20th International Conference on Computational Linguistics, 494--500. Google ScholarDigital Library
- Kaplan, J. and Weinberg, L. 1998. The Emergence of a Euro-American Radical Right., Rutgers University Press, New Brunswick, NJ.Google Scholar
- Kim, S. and Hovy, E. 2004. Determining the sentiment of opinions. In Proceedings of the 20th International Conference on Computational Linguistics, 1367--1373. Google ScholarDigital Library
- Kjell, B. Woods, W. A., and Frieder, O. 1994. Discrimination of authorship using visualization. Inf. Process. Manage. 30, 1, 141--150. Google ScholarDigital Library
- Koppel, M., Argamon, S., and Shimoni, A. R. 2002. Automatically categorizing written texts by author gender. Literar. Linguis. Comput. 17, 4, 401--412.Google ScholarCross Ref
- Koppel, M. and Schler, J. 2003. Exploiting stylistic idiosyncrasies for authorship attribution. In Proceedings of the IJCAI Workshop on Computational Approaches to Style Analysis and Synthesis, Acapulco, Mexico.Google Scholar
- Levine, D. 1996. Application of a hybrid genetic algorithm to airline crew scheduling. Comput. Oper. Res. 23, 6, 547--558. Google ScholarDigital Library
- Leets, L. 2001. Responses to Internet hate sites: Is speech too free in cyberspace? Commun. Law Policy 6, 2, 287--317.Google Scholar
- Li, J., Zheng, R., and Chen, H. 2006. From fingerprint to writeprint. Commun. ACM 49, 4, 76--82. Google ScholarDigital Library
- Li, J., Su, H., Chen, H., and Futscher, B. 2007. Optimal search-based gene subset selection for gene array cancer classification. IEEE Trans. Inf. Technol. Biomed (to appear). Google ScholarDigital Library
- Liu, B., Hu, M., and Cheng, J. 2005. Opinion observer: Analyzing and comparing opinions on the Web. In Proceedings of the 14th International World Wide Web Conference (WWW), 342--351. Google ScholarDigital Library
- Martin, J. R. and White, P. R. R. 2005. The Language of Evaluation: Appraisal in English. Palgrave, London.Google Scholar
- Mishne, G. 2005. Experiments with mood classification. In Proceedings of the 1st Workshop on Stylistic Analysis of Text for Information Access, Salvador, Brazil.Google Scholar
- Mitra, M., Buckley, C., Singhal, A., and Cardie, C. 1997. An analysis of statistical and syntactic phrases. In Proceedings of the 5th International Conference Recherche d'Information Assistee par Ordinateur, Montreal, Canada, 200--214.Google Scholar
- Mladenic, D., Brank, J., Grobelnik, M., and Milic-Frayling, N. 2004. Feature selection using linear classifier weights: Interaction with classification models. In Proceedings of the 27th ACM SIGIR Conference on Research and Development in Information Retrieval, Sheffield, UK, 234--241. Google ScholarDigital Library
- Morinaga, S., Yamanishi, K., Tateishi, K., and Fukushima, T. 2002. Mining product reputations on the Web. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Canada, 341--349. Google ScholarDigital Library
- Mullen, T. and Collier, N. 2004. Sentiment analysis using support vector machines with diverse information sources. In Proceedings of the Empirical Methods in Natural Language Processing (EMNLP) Conference, Barcelona, Spain, 412--418.Google Scholar
- Nasukawa, T. and Yi, J. 2003. Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the 2nd International Conference on Knowledge Capture, Sanibel Island, FL, 70--77. Google ScholarDigital Library
- Nigam, K. and Hurst, M. 2004. Towards a robust metric of opinion. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text.Google Scholar
- Ng, V., Dasgupta, S., and Arifin, S. M. N. 2006. Examining the role of linguistic knowledge sources in the automatic identification and classification of reviews. In Proceedings of the COLING/ACL Conference. Sydney, Australia, 611--618. Google ScholarDigital Library
- Oliveira, L. S., Sabourin, R., Bortolozzi, F., and Suen, C. Y. 2002. Feature selection using multi-objective genetic algorithms for handwritten digit recognition. In Proceedings of the 16th International Conference on Pattern Recognition, 568--571. Google ScholarDigital Library
- Pang, B., Lee, L., and Vaithyanathain, S. 2002. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 79--86. Google ScholarDigital Library
- Pang, B. and Lee, L. 2004. A sentimental education: Sentimental analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, 271--278. Google ScholarDigital Library
- Picard, R. W. 1997. Affective Computing. MIT Press, Cambridge, MA. Google ScholarDigital Library
- Platt, J. 1999. Fast training on SVMs using sequential minimal optimization. In Advances in Kernel Methods: Support Vector Learning. B. Scholkopf et al. Eds., MIT Press, Cambridge, MA, 185--208. Google ScholarDigital Library
- Quinlan, J. R. 1986. Induction of decision trees. Mach. Learn. 1, 1, 81--106. Google ScholarCross Ref
- Riloff, E., Patwardhan, S., and Wiebe, J. 2006. Feature subsumption for opinion analysis. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Sydney, Australia, 440--448. Google ScholarDigital Library
- Riloff, E., Wiebe, J., and Wilson, T. 2003. Learning subjective nouns using extraction pattern bootstrapping. In Proceedings of the 7th Conference on Natural Language Learning, Edmonton, Canada, 25--32. Google ScholarDigital Library
- Robinson, L. 2005. Debating the events of September 11th: Discursive and interactional dynamics in three online for a. J. Comput. Mediat. Commun. 10, 4.Google ScholarCross Ref
- Schafer, J. 2002. Spinning the web of hate: Web-based hate propagation by extremist organizations. J. Criminal Just. Popular Culture 9, 2, 69--88.Google Scholar
- Schler, J., Koppel, M., Argamon, S., and Pennebaker, J. 2006. Effects of age and gender on blogging. In Proceedings of the AAAI Spring Symposium Computational Approaches to Analyzing Weblogs, Menlo Park, CA, 191--197.Google Scholar
- Sebastiani, F. 2002. Machine learning in automated text categorization. ACM Comput. Surv. 34, 1, 1--47. Google ScholarDigital Library
- Shannon, C. E. 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27, 4, 379--423.Google ScholarCross Ref
- Siedlecki, W. and Sklansky, J. 1989. A note on genetic algorithms for large-scale feature selection. Pattern Recogn. Lett. 10, 5, 335--347. Google ScholarDigital Library
- Subasic, P. and Huettner, A. 2001. Affect analysis of text using fuzzy semantic typing. IEEE Trans. Fuzzy Syst. 9, 4, 483--496. Google ScholarDigital Library
- Tong, R. 2001. An operational system for detecting and tracking opinions in on-line discussion. In Proceedings of the ACM SIGIR Workshop on Operational Text Classification, 1--6.Google Scholar
- Turney, P. D. 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meetings of the Association for Computational Linguistics, Philadelphia, PA, 417--424. Google ScholarDigital Library
- Turney, P, D. and Littman, M. L. 2003. Measuring praise and criticism: Inference of semantic orientation from association. ACM Trans. Inf. Syst. 21, 4, 315--346. Google ScholarDigital Library
- Vafaie, H. and Imam, I. F. 1994. Feature selection methods: Genetic algorithms vs. greedy-like search. In Proceedings of the International Conference on Fuzzy and Intelligent Control Systems.Google Scholar
- Viegas, F. B. and Smith, M. 2004. Newsgroup crowds and AuthorLines: Visualizing the activity of individuals in conversational cyberspaces. In Proceedings of the 37th Hawaii International Conference on System Sciences, Hawaii, USA. Google ScholarDigital Library
- Whitelaw, C., Garg, N., and Argamon, S. 2005. Using appraisal groups for sentiment analysis. In Proceedings of the 14th ACM Conference on Information and Knowledge Management, 625--631. Google ScholarDigital Library
- Wiebe, J. 1994. Tracking point of view in narrative. Comput. Linguist. 20, 2, 233--287. Google ScholarDigital Library
- Wiebe, J., Wilson, T., and Bell, M. 2001. Identifying collocations for recognizing opinions. In Proceedings of the ACL/EACL Workshop on Collocation, Toulouse, France.Google Scholar
- Wiebe, J., Wilson, T., Bruce, R., Bell, M., and Martin, M. 2004. Learning subjective language. Comput. Linguist. 30, 3, 277--308. Google ScholarDigital Library
- Wiebe, J., Wilson, T., and Cardie, C. 2005. Annotating expressions of opinions and emotions in language. Lang. Resources Eval. 1, 2,165--210.Google ScholarCross Ref
- Witten, I. H. and Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. Morgan Kaufmann, San Francisco, CA. Google ScholarDigital Library
- Wilson, T., Wiebe, J., and Hoffman, P. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, British Columbia, Canada, 347--354. Google ScholarDigital Library
- Yang, Y. and Pederson, J. O. 1997. A comparative study on feature selection in text categorization. In Proceedings of the 14th International Conference on Machine Learning, 412--420. Google ScholarDigital Library
- Yang, J. and Honavar, V. 1998. Feature subset selection using a genetic algorithm. IEEE Intell. Syst. 13, 2, 44--49. Google ScholarDigital Library
- Yi, J., Nasukawa, T., Bunescu, R., and Niblack, W. 2003. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In Proceedings of the 3rd IEEE International Conference on Data Mining, 427--434. Google ScholarDigital Library
- Yi, J. and Niblack, W. 2005. Sentiment mining in WebFountain. In Proceedings of the 21st International Conference on Data Engineering, 1073--1083. Google ScholarDigital Library
- Yu, H. and Hatzivassiloglou, V. 2003. 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, 129--136. Google ScholarDigital Library
- Zheng, R., Li, J., Huang, Z., and Chen, H. 2006. A framework for authorship analysis of online messages: Writing-Style features and techniques. J. Amer. Soc. Inf. Sci. Technol. 57, 3, 378--393. Google ScholarDigital Library
- Zhou, Y., Reid, E., Qin, J., Chen, H., and Lai, G. 2005. U.S. extremist groups on the Web: Link and content analysis. IEEE Intell. Syst. 20, 5, 44--51. Google ScholarDigital Library
Index Terms
- Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums
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