Advertisement

Dark Web pp 171-201 | Cite as

Sentiment Analysis

  • Hsinchun ChenEmail author
Chapter
Part of the Integrated Series in Information Systems book series (ISIS, volume 30)

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 the key features. The proposed features and techniques are evaluated on US and Middle Eastern Web forum postings. The experimental results using EWGA with SVM indicate high performance levels, with accuracy over 95% on the benchmark dataset and over 93% for both the US and Middle Eastern forums. Stylistic features significantly enhanced performance across all test beds while EWGA also outperformed other feature selection methods, indicating the utility of these features and techniques for document-level classification of sentiments.

Keywords

Feature Selection Information Gain Sentiment Analysis Solution String Syntactic Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Abbasi, A., and Chen, H. 2005. Identification and comparison of extremist-group web forum messages using authorship analysis, IEEE Intelligent Systems 20, 5, 67–75.CrossRefGoogle Scholar
  2. Abbasi, A., and Chen, H. 2006. Visualizing authorship for identification, In Proceedings of the 4thIEEE International Conference on Intelligence and Security Informatics, San Diego, CA, 60–71.Google Scholar
  3. Abbasi, A., and Chen, H. 2007. Affect intensity analysis of Dark Web forums, In Proceedings of the 5thIEEE International Conference on Intelligence and Security Informatics, New Brunswick, NJ, 282–288.Google Scholar
  4. Abbasi, A., and Chen, H. 2008. Analysis of affect intensities in extremist group forums, In Terrorism Informatics, (Eds.) H. Chen, E. Reid, H. Chen, J. Sinai, A. Silke, B. Ganor, Springer-Verlag. Google Scholar
  5. 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, European Journal of Operational Research 134, 165–178. CrossRefzbMATHGoogle Scholar
  6. Aggarwal, C.C., Orlin, J., and Tai, R.P. 1997. Optimized crossover for the independent set problem, Operations Research 45, 2, 226–234. MathSciNetCrossRefzbMATHGoogle Scholar
  7. Agrawal, R., Rajagopalan, S., Srikant, R. and Xu, Y. 2003. Mining newsgroups using networks arising from social behavior, In Proceedings of the 12thInternational World Wide Web Conference, 529–535. Google Scholar
  8. Balakrishnan, P.V., Gupta, R., and Jacob, V.S. 2004. Development of hybrid genetic algorithms for product line designs, IEEE Transactions on Systems, Man, and Cybernetics 34, 1, 468–483. CrossRefGoogle Scholar
  9. Beineke, P., Hastie, T., and Vaithyanathan, S. 2004. The sentimental factor: Improving review classification via human-provided information, In Proceedings of the 42ndAnnual Meeting of the Association for Computational Linguistics, 263. Google Scholar
  10. Burris, V., Smith, E. and Strahm, A. 2000. White supremacist networks on the Internet, Sociological Focus 33, 2, 215–235. CrossRefGoogle Scholar
  11. Chen, A. and Gey, F. 2002. Building an Arabic stemmer for information retrieval, In Proceedings of the 11thText Retrieval Conference, Gaithersburg, MD, 631–639. Google Scholar
  12. Chen, H. 2006. Intelligence and Security Informatics for International Security: Information Sharing and Data Mining, London, Springer Press.CrossRefGoogle Scholar
  13. Crilley, K. 2001. Information warfare: New battle fields, terrorists, propaganda, and the Internet, Aslib Proceedings 53, 7, 250–264. CrossRefGoogle Scholar
  14. Dash, M. and Liu, H. 1997. Feature selection for classification, Intelligent Data Analysis 1, 131–156. CrossRefGoogle Scholar
  15. 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 12thInternational Conference on the World Wide Web, 519–528. Google Scholar
  16. De Vel, O., Anderson, A., Corney, M., and Mohay, G. 2001. Mining e-mail content for author identification forensics, ACM SIGMOD Record 30, 4, 55–64.CrossRefGoogle Scholar
  17. Donath, J. 1999. Identity and deception in the virtual community, In Kollock, P., and Smith, M. (Eds.), Communities in Cyberspace, London: Routledge, 27–58. Google Scholar
  18. 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
  19. Efron, M., Marchionini, G., and Zhiang, J. 2003. Implications of the recursive representation problem for automatic concept identification in on-line government information, In Proceedings of the ASIST SIG-CR Workshop.Google Scholar
  20. Fei, Z., Liu, J., and Wu, G. 2004. Sentiment classification using phrase patterns, In Proceedings of the 4thIEEE International Conference on Computer Information Technology, 1147–1152.Google Scholar
  21. Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification, Journal of Machine Learning Research 3, 1289–1305.zbMATHGoogle Scholar
  22. 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 Scholar
  23. Glaser, J., Dixit, J., and Green, D. P. 2002. Studying hate crime with the Internet: What makes racists advocate racial violence? Journal of Social Issues 58, 1, 177–193.CrossRefGoogle Scholar
  24. 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
  25. Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. 2002. Gene selection for cancer classification using support vector machines, Machine Learning46, 389–422.CrossRefzbMATHGoogle Scholar
  26. Guyon, I., and Elisseeff, A. 2003. An introduction to variable and feature selection, Journal of Machine Learning Research 3, 1157–1182.zbMATHGoogle Scholar
  27. Hatzivassiloglou, V. and McKeown, K. R. 1997. Predicting the semantic orientation of adjectives, In Proceedings of the 35thAnnual Meeting of the Association of Computational Linguistics, 174–181.Google Scholar
  28. Hearst, M. A. 1992. Direction-based text interpretation as an information access refinement. In P. Jacobs (Ed.), Text-Based Intelligent Systems: Current Research and Practice in Information Extraction and Retrieval. Mahwah, NJ, Lawrence Erlbaum Associates.Google Scholar
  29. 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 and Society 13, 1, 75–104.CrossRefGoogle Scholar
  30. Herring, S., Job-Sluder, K., Scheckler, R., and Barab, S. 2002. Searching for safety online: Managing “trolling” in a feminist forum, The Information Society 18, 5, 371–384.CrossRefGoogle Scholar
  31. Herring, S. and Paolillo, J. C. 2006. Gender and genre variations in weblogs, Journal of Sociolinguistics, 10, 4, 439.CrossRefGoogle Scholar
  32. Holland, J. 1975.Adaptation in natural and artificial systems. Ann Arbor, University of Michigan Press.Google Scholar
  33. Hu, M. and Liu, B. 2004. Mining and summarizing customer reviews. In Proceedings of the ACM SIGKDD International Conference, 168–177.Google Scholar
  34. Jain, A. and Zongker, D. 1997. Feature selection: Evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 2, 153–158.CrossRefGoogle Scholar
  35. 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.CrossRefGoogle Scholar
  36. Juola, P. and Baayen, H. 2005. A controlled-corpus experiment in authorship identification by cross-entropy, Literary and Linguistic Computing 20, 59–67.CrossRefGoogle Scholar
  37. 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 Scholar
  38. Kaplan, J., and Weinberg, L. 1998.The Emergence of a Euro-American Radical Right., New Brunswick, NJ, Rutgers University Press.Google Scholar
  39. Kim, S. and Hovy, E. 2004. Determining the sentiment of opinions, In Proceedings of the 20th International Conference on Computational Linguistics, 1367–1373.Google Scholar
  40. Kjell, B., Woods, W.A., and Frieder, O. 1994. Discrimination of authorship using visualization, Information Processing and Management 30, 1, 141–150.CrossRefGoogle Scholar
  41. Koppel, M., Argamon, S., and Shimoni, A.R. 2002. Automatically categorizing written texts by author gender, Literary and Linguistic Computing 17, 4, 401–412.CrossRefGoogle Scholar
  42. 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
  43. Levine, D. 1996. Application of a hybrid genetic algorithm to airline crew scheduling, Computers and Operations Research 23, 6, 547–558.CrossRefzbMATHGoogle Scholar
  44. Leets, L. 2001. Responses to Internet hate sites: Is speech too free in cyberspace? Communication Law and Policy 6, 2, 287–317.CrossRefGoogle Scholar
  45. Li, J., Zheng, R., and Chen, H. 2006. From fingerprint to writeprint, Communications of the ACM 49, 4, 76–82.CrossRefGoogle Scholar
  46. Li, J. Su, H., Chen, H., and Futscher, B. 2007. Optimal search-based gene subset selection for gene array cancer classification, IEEE Transactions on Information Technology in Biomedicine 11, 4, 398–405.CrossRefGoogle Scholar
  47. 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, 342–351.Google Scholar
  48. Martin, J. R. and White, P.R.R. 2005. The Language of Evaluation: Appraisal in English, London, Palgrave.CrossRefGoogle Scholar
  49. Mishne, G. 2005. Experiments with mood classification, In Proceedings of the 1stWorkshop on Stylistic Analysis of Text for Information Access, Salvador, Brazil.Google Scholar
  50. 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
  51. 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 27thACM SIGIR Conference on Research and Development in Information Retrieval, Sheffield, UK, 234–241.Google Scholar
  52. Morinaga, S., Yamanishi, K., Tateishi, K., and Fukushima, T. 2002. Mining product reputations on the web, In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Canada, 341–349.Google Scholar
  53. 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, Barcelona, Spain, 412–418.Google Scholar
  54. 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, Florida, 70–77.Google Scholar
  55. 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
  56. 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 Scholar
  57. 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 Scholar
  58. Pang, B., and Lee, L. 2004. A sentimental education: Sentimental analysis using subjectivity summarization based on minimum cuts, In Proceedings of the 42ndAnnual Meeting of the Association for Computational Linguistics, 271–278.Google Scholar
  59. Peng, F., Schuurmans, D., Keselj, V., and Wang, S. 2003. Automated authorship attribution with character level language models. Paper presented at the 10th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2003).Google Scholar
  60. Picard, R. W. 1997. Affective Computing, Cambridge, MA, MIT Press.Google Scholar
  61. Platt, J. 1999. Fast training on SVMs using sequential minimal optimization, In Scholkopf, B., Burges, C., and Smola, A. (Ed.), Advances in Kernel Methods: Support Vector Learning, Cambridge, MA, MIT Press, 185–208.Google Scholar
  62. Quinlan, J. R. 1986. Induction of decision trees, Machine Learning 1, 1, 81–106.Google Scholar
  63. Riloff, E., Wiebe, J., and Wilson, T. 2003. Learning subjective nouns using extraction pattern bootstrapping, In Proceedings of the Seventh Conference on Natural Language Learning Conference, Edmonton, Canada, 25–32.Google Scholar
  64. Robinson, L. 2005. Debating the events of September 11th: Discursive and interactional dynamics in three online for a, Journal of Computer-Mediated Communication 10, 4.CrossRefGoogle Scholar
  65. Schafer, J. 2002. Spinning the web of hate: Web-based hate propagation by extremist organizations, Journal of Criminal Justice and Popular Culture 9, 2, 69–88.Google Scholar
  66. 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
  67. Sebastiani, F. 2002. Machine learning in automated text categorization, ACM Computing Surveys 34, 1, 1–47.MathSciNetCrossRefGoogle Scholar
  68. Shannon, C. E. 1948. A mathematical theory of communication, Bell System Technical Journal 27, 4, 379–423.MathSciNetCrossRefGoogle Scholar
  69. Siedlecki, W. and Sklansky, J. 1989. A note on genetic algorithms for large-scale feature selection, Pattern Recognition Letters 10, 5, 335–347.CrossRefzbMATHGoogle Scholar
  70. Stamatatos, E., Fakotakis, N., & Kokkinakis, G. 2001. Computer-based authorship attribution without lexical measures. Computers and the Humanities 35, 2, 193–214.CrossRefzbMATHGoogle Scholar
  71. Subasic, P., and Huettner, A. 2001. Affect analysis of text using fuzzy semantic typing, IEEE Transactions on Fuzzy Systems 9, 4, 483–496.CrossRefGoogle Scholar
  72. 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
  73. 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 Scholar
  74. Turney, P, D., and Littman, M, L. 2003. Measuring praise and criticism: Inference of semantic orientation from association, ACM Transactions on Information Systems 21, 4, 315–346.CrossRefGoogle Scholar
  75. 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, 1994.Google Scholar
  76. 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 Scholar
  77. Whitelaw, C., Garg, N., and Argamon, S. 2005. Using appraisal groups for sentiment analysis, In Proceedings of the 14thACM Conference on Information and Knowledge Management, 625–631.Google Scholar
  78. Wiebe, J. 1994. Tracking point of view in narrative, Computational Linguistics 20, 2, 233–287.Google Scholar
  79. 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
  80. Wiebe, J., Wilson, T., Bruce, R., Bell, M., and Martin, M. 2004. Learning subjective language, Computational Linguistics 30, 3, 277–308.CrossRefGoogle Scholar
  81. Wiebe, J., Wilson, T., and Cardie, C. 2005. Annotating expressions of opinions and emotions in language, Language Resources and Evaluation 1, 2, 165–210.CrossRefGoogle Scholar
  82. Witten, I. H., and Frank, E. 2005.Data Mining: Practical machine learning tools and techniques, 2nd Edition,, San Francisco, CA, Morgan Kaufmann.zbMATHGoogle Scholar
  83. 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 Scholar
  84. Yang, Y. and Pederson, J. O. 1997. A comparative study on feature selection in text categorization, In Proceedings of the 14thInternational Conference on Machine Learning, 412–420.Google Scholar
  85. Yang, J. and Honavar, V. 1998. Feature subset selection using a genetic algorithm, IEEE Intelligent Systems 13, 2, 44–49.CrossRefGoogle Scholar
  86. 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 3 rd IEEE International Conference on Data Mining, 427–434.Google Scholar
  87. 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 Scholar
  88. Zheng, R., Li, J., Huang, Z., and Chen, H. 2006. A framework for authorship analysis of online messages: Writing-style features and techniques, Journal of the American Society for Information Science and Technology 57, 3, 378–393.CrossRefGoogle Scholar
  89. Zhou, Y., Reid, E., Qin, J., Chen, H., and Lai, G. 2005. U.S. extremist groups on the web: Link and content analysis, IEEE Intelligent Systems 20, 5, 44–51.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Management Information SystemsUniversity of ArizonaTusconUSA

Personalised recommendations