Opinion Mining Using Sequence Labeling
AbstractOpinion mining aims to determine the attitude of a person by identifying and extracting subjective information. The attitude is the judgement, evaluation or emotional state of the person towards a product, or service or a person. An essential task in opinion mining is to classify the polarity of a review at the document, sentence, or feature level whether the expressed opinion is positive, negative or neutral. The main objective of this research work is to formulate opinion mining task as sequence labelling and to build the models for classifying the opinion about the product Kingston Pen drive review as positive or negative. The performances are evaluated and the comparative results are analyzed and reported.
Jiawei Han, MichelineKamber, Data Mining: Concepts and Techniques. second edition.
Mitchell T, Machine learning. Ed McGraw-Hill International edition.
Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classiﬁcation of product reviews. In: 12th International Conference on World Wide Web, 2002, pp. 519–528.
Eibe Frank, Ian H. Witten, DataMining – Practical Machine Learning Tools and Techniques. 2005.
E. Brill. Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging. Comput. Linguist., 1995, 21:p.543–565.
Xia, Y. Q., Xu, R. F., Wong, K. F., and Zheng, F. The unified collocation framework for opinion mining. Proc. IEEE Int. Conf. on Machine Learning and Cybernetics, 2007.
A. Harb, M. Planti, G. Dray, M. Roche, Fran, o. Trousset, and P. Poncelet, Web opinion mining: how to extract opinions from blogs?. 2008.
M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, Lexicon-based methods for sentiment analysis. Comput.Linguist., 2011, vol.37, pp. 267-307.
Ting-Chun Peng; Chia-Chun Shih, Using Chinese part-of-speech patterns for sentiment phrase identification and opinion extraction in user generated reviews. Digital Information Management.
Turney, P., and Littman, M. L., Unsupervised learning of semantic orientation. Hundred-billion-word corpus, 2002.
Valarmathi, B., and Palanisamy, V. Opinion Mining Classification Using Key Word Summarization Based on Singular Value Decomposition. International Journal on computer science and Engineering, 2011, Vol.3, No.1, pp.212-215.
S.Kabinsingha,S., Chindasorn,C., and Chantrapornchai.A, Movie Rating Approach and Application Based on Data Mining. International Journal of Engineering and Innovative Technology, 2012, Vol. 2, No.1, pp.77-83.
Tetsuya Nasukawa, Jeonghee Yi, Sentiment Analysis: Capturing Favorability Using Natural Language Processing, K-CAP’03.
Bing Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, 2012.
Lee L. Pang, B and S. Vaithyanathan. Thumbs up? Sentiment classification using machine learning techniques. In EMNLP, 2002, pp 79–86.
John, L., Andrew, M., Fernando.P, Conditional random ﬁelds: probabilistic models for segmenting and labeling sequence data. In: International Conference on Machine Learning, 2001, pp. 282–289.
Luole Qi and Li Chen, A Linear-Chain CRF-Based Learning Approach for Web Opinion Mining. WISE 2010, LNCS 6488, 2010, pp. 128–141.
NL Processor– Text Analysis Toolkit 2000. http://www.infogistics.com/textanalysis.html
Scaffidi, C., Bierhoff, K., Chang, E., Felker, M., Ng, H., Jin, C.: Red Opal: product feature scoring from reviews. In: 8th ACM Conference on Electronic Commerce, 2007, pp. 182–191.
Wilson, T., Hoffmann, P., Soma sundaran, S., Kessler, J., Wiebe, J., Choi, Y., Cardie, C., Riloff, E., Patwardhan, S.: OpinionFinder: a system for subjectivity analysis. In: HLT/EMNLP on Interactive Demonstrations, 2005, pp. 34–35.
Arun K. Pujari, Data Mining Techniques. Universities of Press, 2010.