ICTACT Journal on Soft Computing

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A Hybrid Approach for Polarity Shift Detection


Affiliations
1 Department of Information Technology, G.H. Patel College of Engineering and Technology, India
     

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Now-a-days sentiment analysis has become a hot research area. With the increasing use of internet, people express their views by using social media, blogs, etc. So there is a dire need to analyze people's opinions. Sentiment classification is the main task of sentiment analysis. But while classifying sentiments, the problem of polarity shift occurs. Polarity shift is considered as a very crucial problem. Polarity shift changes a text from positive to negative and vice versa. In this paper, a hybrid approach is proposed for polarity shift detection of negation (explicit and implicit) and contrast. The hybrid approach consists of a rule-based approach for detecting explicit negation and contrast and a lexicon called SentiWordNet for detecting implicit negation. The proposed approach outperforms its baselines.

Keywords

Sentiment Analysis, Sentiment Classification, Polarity Shift, Natural Language Processing, Lexicon.
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  • Bing Liu, “Sentiment Analysis and Opinion Mining Synthesis Lectures on Human Language Technologies”, Morgan and Claypool Publishers, 2012.

  • Bo Pang, Lillian Lee and Shivakumar Vaithyanathan. “Thumbs up? : Sentiment Classification using Machine Learning Techniques”, Proceedings of ACL Conference on Empirical Methods in Natural Language Processing, Vol.10, pp. 79-86, 2002.

  • Rui Xia et al., “Polarity Shift Detection, Elimination and Ensemble: A Three-Stage Model for Document-Level Sentiment Analysis”, Information Processing and Management, Vol. 52, No. 1, pp. 36-45, 2016.

  • Rui Xia et al., “Dual Sentiment Analysis: Considering Two Sides of One Review”, IEEE Transactions on Knowledge and Data Engineering, Vol. 27, No. 8, pp. 2120-2133, 2015.

  • Livia Polanyi and Annie Zaenen, “Contextual Lexical Valence Shifters”, Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text, pp. 16, 2004.

  • Shoushan Li et al., “Sentiment Classification and Polarity Shifting”, Proceedings of the 23rd International Conference on Computational Linguistics Association for Computational Linguistics, pp. 635-643, 2010.

  • Shoushan Li, Zhongqing Wang, Sophia Yat Mei Lee and Chu-Ren Huang, “Sentiment Classification with Polarity Shifting Detection”, Proceedings of IEEE International Conference on Asian Language Processing, pp. 129-132, 2013.

  • Peter D. Turney, “Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews”, Proceedings of 40th Annual Meeting on Association for Computational Linguistics Association for Computational Linguistics, pp. 417-424, 2002.

  • Sanjiv Das and Mike Chen, “Yahoo! for Amazon: Extracting Market Sentiment from Stock Message Boards”, Proceedings of the Asia Pacific Finance Association Annual Conference, Vol. 35, pp. 1-6, 2001.

  • Xiaoqian Zhang, Shoushan Li, Guodong Zhou and Hongxia Zhao, “Polarity Shifting: Corpus Construction and Analysis”, Proceedings of International Conference on Asian Language Processing, pp. 272-275, 2011.

  • Xiaowen Ding, Bing Liu and Philip S. Yu, “A Holistic Lexicon-based Approach to Opinion Mining”, Proceedings of ACM International Conference on Web Search and Data Mining, pp. 231-240, 2008.

  • Xiaojun Wan, “Co-Training for Cross-Lingual Sentiment Classification”, Proceedings of the Joint Conference 47th Annual Meeting of the ACL and 4th International Joint Conference on Natural Language Processing, Vol. 1, pp.235-243, 2009.

  • Maite Taboada et al., “Lexicon-based Methods for Sentiment Analysis”, Computational Linguistics, Vol. 37, No. 2, pp. 267-307, 2011.

  • Xiaowen Ding and Bing Liu, “The Utility of Linguistic Rules in Opinion Mining”, Proceedings of 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 811-812, 2007.

  • Hassan Saif, Miriam Fernandez, Yulan He and Harith Alani, “Senticircles for Contextual and Conceptual Semantic Sentiment Analysis of Twitter”, European Semantic Web Conference, pp. 83-98, 2014.

  • Shoushan Li and Chengqing Zong, “Multi-Domain Sentiment Classification”, Proceedings of 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers, pp. 257-260, 2008.

  • Andrea Esuli and Fabrizio Sebastiani, “Sentiwordnet: A Publicly Available Lexical Resource for Opinion Mining”, Proceedings of 5th Conference on Language Resources and Evaluation, pp. 417-422, 2006.

  • Orestes Appel et al., “A Hybrid Approach to the Sentiment Analysis Problem at the Sentence Level”, Knowledge-Based Systems, Vol. 108, pp. 110-124, 2016.

  • John Blitzer, Mark Dredze and Fernando Pereira, “Biographies, Bollywood, Boom-Boxes and Blenders: Domain Adaptation for Sentiment Classification”, Proceedings of 45th Annual Meeting of the Association of Computational Linguistics, pp. 440-447, 2007.

  • Minqing Hu and Bing Liu, “Mining and Summarizing Customer Reviews”, Proceedings of 10th ACM International Conference on Knowledge Discovery and Data Mining, pp.168-177, 2004.

  • Jin-Cheon Na et al., “Effectiveness of Simple Linguistic Processing in Automatic Sentiment Classification of Product Reviews”, Proceedings of International Conference on International Society for Knowledge Organization, pp. 4954, 2004.

  • Sajib Dasgupta and Vincent Ng, “Mine the Easy, Classify the Hard: A Semi-Supervised Approach to Automatic Sentiment Classification”, Proceedings of Joint Conference on 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing, pp. 701709, 2009.

  • Minqing Hu and Bing Liu, “Mining Opinion Features in Customer Reviews”, American Association for Artificial Intelligence, Vol. 4, No. 4, pp. 755-760, 2004.

  • Xiaowen Ding, Bing Liu and Lei Zhang, “Entity Discovery and Assignment for Opinion Mining Applications”, Proceedings of 15th ACM International Conference on Knowledge Discovery and Data Mining, pp. 1125-1134, 2009.

  • Soo-Min Kim and Eduard Hovy, “Determining the Sentiment of Opinions”, Proceedings of 20th International Conference on Computational Linguistics, pp. 1-7, 2004.

  • Alistair Kennedy and Diana Inkpen, “Sentiment Classification of Movie Reviews using Contextual Valence Shifters”, Computational Intelligence, Vol. 22, No. 2, pp.110-125, 2006.

  • Peter D. Turney and Michael L. Littman, “Measuring Praise and Criticism: Inference of Semantic Orientation from Association”, ACM Transactions on Information Systems, Vol. 21, No. 4, pp. 315-346, 2003.

  • Theresa Wilson, Janyce Wiebe and Paul Hoffmann, “Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis”, Computational Linguistics, Vol. 35, No. 3, pp. 399-433, 2009.

  • Kushal Dave, Steve Lawrence and David M. Pennock, “Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews”, Proceedings of 12th International Conference on World Wide Web, pp.519-528, 2003.

  • J. Yi, T. Nasukawa, R. Bunescu and W. Niblack, “Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques”, Proceedings of 3rd IEEE International Conference on Data Mining, pp. 1-8, 2003.

  • Satoshi Morinaga et al., “Mining Product Reputations on the Web”, Proceedings of 8th ACM International Conference on Knowledge Discovery and Data Mining, pp. 341-349, 2002.

  • Simon Tong and Daphne Koller, “Support Vector Machine Active Learning with Applications to Text Classification”, Journal of Machine Learning Research, Vol. 2, pp. 45-66, 2001.

  • Janyce M. Wiebe, “Learning Subjective Adjectives from Corpora”, Proceedings of International Conference on American Association for Artificial Intelligence, pp. 1-6, 2000.

  • Erik Cambria and Bebo White, “Jumping NLP Curves: A Review of Natural Language Processing Research”, IEEE Computational Intelligence Magazine, Vol. 9, No. 2, pp. 4857, 2014.

  • J. Yi, T. Nasukawa, R. Bunescu and W. Niblack, “Sentiment Analyzer: Extracting Sentiments about A given Topic using Natural Language Processing Techniques”, Proceedings of 3rd IEEE International Conference on Data Mining, pp. 1-8, 2003.

  • Rui Xia et al., “Dual Training and Dual Prediction for Polarity Classification”, Proceedings of 51st Annual Meeting of the Association for Computational Linguistics, pp. 521525, 2013.

  • Erik Cambria, “Affective Computing and Sentiment Analysis”, IEEE Intelligent Systems, Vol. 31, No. 2, pp. 102107, 2016.

  • Bo Pang and Lillian Lee, “Opinion Mining and Sentiment Analysis”, Foundations and Trends in Information Retrieval, Vol. 2, No. 1-2, pp. 1-135, 2008.

  • Erik Cambria, Daniel Olsher and Dheeraj Rajagopal, “SenticNet 3: A Common and Common-Sense Knowledge base for Cognition-Driven Sentiment Analysis”, Proceedings of 28th Conference on Artificial Intelligence, pp. 1515-1521, 2014.

  • Bo Pang and Lillian Lee, “Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales”, Proceedings of 43rd Annual Meeting on Association for Computational Linguistics, pp. 115-124, 2005.

  • Hassan Saif, Yulan He and Harith Alani, “Alleviating Data Sparsity for Twitter Sentiment Analysis”, Proceedings of CEUR Workshop, pp. 1-8, 2012.

  • Mike Thelwall, Kevan Buckley, Georgios Paltoglou, Di Cai and Arvid Kappas, “Sentiment Strength Detection in Short Informal Text”, Journal of the American Society for Information Science and Technology, Vol. 61, No. 12, pp.2544-2558, 2010.


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  • A Hybrid Approach for Polarity Shift Detection

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Authors

Michele Mistry
Department of Information Technology, G.H. Patel College of Engineering and Technology, India
Prem Balani
Department of Information Technology, G.H. Patel College of Engineering and Technology, India

Abstract


Now-a-days sentiment analysis has become a hot research area. With the increasing use of internet, people express their views by using social media, blogs, etc. So there is a dire need to analyze people's opinions. Sentiment classification is the main task of sentiment analysis. But while classifying sentiments, the problem of polarity shift occurs. Polarity shift is considered as a very crucial problem. Polarity shift changes a text from positive to negative and vice versa. In this paper, a hybrid approach is proposed for polarity shift detection of negation (explicit and implicit) and contrast. The hybrid approach consists of a rule-based approach for detecting explicit negation and contrast and a lexicon called SentiWordNet for detecting implicit negation. The proposed approach outperforms its baselines.

Keywords


Sentiment Analysis, Sentiment Classification, Polarity Shift, Natural Language Processing, Lexicon.

References