Abstract
We address in this paper the adaptation problem in sentiment classification. As we know, available labeled data required by sentiment classifiers does not always exist. Given a set of labeled data from different domains and a collection of unlabeled data of the target domain, it would be interesting to determine which subset of those domains has a feature distribution similar to the target domain. In this way, in the absence of labeled data for a particular target domain, it would be plausible to make use of the labeled data corresponding to the most similar domains.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Aue, A., Gamon, M.: Customizing sentiment classifiers to new domains: A case study. In: Proceedings of Recent Advances in Natural Language Processing (RANLP), Borovets, Bulgaria (2005)
Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, 1st edn. Springer (2007)
Brazdil, P., Giraud-Carrier, C., Soares, C., Vilalta, R.: Metalearning Applications to Data Mining, 1st edn. Springer (2009)
Blitzer, J., Dredze, M., Pereira, F.: Biographies, Bollywood, Boom-boxes and Blenders: Domain adaptation for sentiment classification. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL), Prague, Czech Republic (2007)
Chesley, P., Vincent, B., Xu, L., Srihari, R.: Using verbs and adjectives to automatically classify blog sentiment. In: AAAI Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW), pp. 74–80 (2006)
Escalante, H.J., Montes, M., Solorio, T.: A Weighted Profile Intersection Measure for Profile-based Authorship Attribution. In: Proceedings of 10th Mexican International Conference on Artificial Intelligence (MICAI), Puebla, Mexico (2011)
Esuli, A., Sebastiani, F.: SentiWordNet: a publicly available lexical resource for opinion mining. In: Proceedings of Fifth International Conference on Language Resources and Evaluation (LREC), Genoa, Italy, vol. 6 (2006)
Heck, T.: A Comparison of Different User-Similarity Measures as Basis for Research and Scientific Cooperation. In: International Conference on Information Science and Social Media, bo/Turku, Finland (2011)
Keefe, T., Koprinska, I.: Feature Selection and Weighting Methods in Sentiment Analysis. In: Proceedings of the 14th Australasian Document Computing Symposium, Sydney, Australia (2009)
Keselj, V., Peng, F., Cercone, N., Thomas, C.: N-gram-based author profiles for authorship attribution. In: Proceedings of the Pacific Association for Computational Linguistics, Halifax, Canada, pp. 255–264 (2003)
Liu, B.: Sentiment Analysis and Subjectivity. In: Indurkhya, N., Damerau, F.J. (eds.) Handbook of Natural Language Processing, pp. 627–665. Chapman and Hall/CRC (2010)
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), Philadelphia, USA, pp. 79–86 (2002)
Pang, B., Lee, L.: A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL), Barcelona, Spain, pp. 21–26 (2004)
Shou-Shan, L., Chu-Ren, H., Cheng-Qing, Z.: Multi-Domain Sentiment Classification with Classifier Combination. Journal of Computer Science and Technology 26(1), 25–33 (2011)
Steck, J.B.: Netpix: A Method of Feature Selection Leading to Accurate Sentiment-Based Classification Models. Master thesis, Central Connecticut State University (2005)
Stamatatos, E.: A survey of modern authorship attribution methods. Journal of the American Society for Information Science and Technology 60(3), 538–556 (2009)
Taboada, M., Anthony, C., Voll, K.: Creating Semantic Orientation Dictionaries. In: Proceedings of Fifth International Conference on Language Resources and Evaluation (LREC), Genoa, Italy, pp. 427–432 (2006)
Toutanova, K., Klein, D., Manning, C., Singer, Y.: Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In: Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL), Edmonton, Canada, pp. 252–259 (2003)
Turney, P.: Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, USA, pp. 417–424 (2002)
Yang, H., Si, L., Callan, J.: Knowledge transfer and opinion detection in the TREC2006 blog track. In: Proceedings of the Fifteenth Text REtrieval Conference, Gaithersburg, MD (2006)
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
Uribe, D. (2013). Measuring Feature Distributions in Sentiment Classification. In: Batyrshin, I., Mendoza, M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-37798-3_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37797-6
Online ISBN: 978-3-642-37798-3
eBook Packages: Computer ScienceComputer Science (R0)