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Sentiment Classification of Context Dependent Words

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Proceedings of International Conference on ICT for Sustainable Development

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 408))

Abstract

With the increase in the use of Web 2.0, there are lot of opinions on the web about any product. Most of the opinions contain opinion words which has same polarity in all contexts. But there are some opinion words called context dependent words which have different polarity in different context. So there is a need to determine the polarity of ambiguous words (context dependent words) efficiently and effectively. This task is also known as word polarity disambiguation (WPD). This literature survey is done to familiarize with general applications and approaches of opinion mining then it presents the context dependent word polarity problem in depth by explaining the existing literature of sentiment classification of context dependent words and finally, some open problems, conclusion, and future directions are discussed.

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References

  1. Liu, B. (2010). Sentiment analysis and subjectivity. In N. Indurkhya & F. Damerau, (Eds.), Handbook of Natural Language Processing (2nd edn.).

    Google Scholar 

  2. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval., 2(1/2), 1–135.

    Article  Google Scholar 

  3. Kaur, A., & Duhan, N. (2015). A survey on sentiment analysis and opinion mining. International Journal of Innovations & Advancement in Computer Science, 107–116.

    Google Scholar 

  4. Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82–89.

    Article  Google Scholar 

  5. Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In C. C. Aggarwal & C. X. Zhai (Eds.), Mining Text Data (pp. 415–163).New York: Springer.

    Google Scholar 

  6. Chandrakala, C. S., & Sindhu, C. (2012). Opinion mining and sentiment classification: A survey. ICTACT Journal on Soft Computing, 3(1), 420–425.

    Google Scholar 

  7. Rashid, A., et al. (2013). A survey paper: areas, techniques and challenges of opinion mining. International Journal of Computer Science (IJCSI), 10(6), 2.

    Google Scholar 

  8. Ding, X., Liu, B., & Yu, P. S. (2008). A holistic lexicon-based approach to opinion mining. In Proceedings of the 2008 International Conference on Web Search and Data Mining (pp 231–240).

    Google Scholar 

  9. Matineau, J., & Finin, T. (2009). Delta tfidf:an improved feature space for sentiment analysis. In Proceedings of the Third International ICWSM Conference (pp. 258–261).

    Google Scholar 

  10. Yuming, L., Zhang, J., Wang, X., & Zhou, A. (2012). An information theoretic approach to sentiment polarity classification. In Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality (pp. 35–40). ACM.

    Google Scholar 

  11. Farag, S., & Mathiak, B. (2013). Revised mutual information approach for german text sentiment classification. In Proceedings of the 22nd International Conference on World Wide Web Companion (pp. 579–586).

    Google Scholar 

  12. Kansal, H., & Toshniwal, D. (2014). Aspect based summarization of context dependent opinion words. 18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems—KES (pp. 166–177).

    Google Scholar 

  13. Rosario, B., Rajon, C., & Cheng, C. K. (2011). Word sense disambiguation of opinionated words using extended gloss overlap. In Proceedings of the 8th Natural Language Processing (pp 1–5).

    Google Scholar 

  14. Jalilvand, A., & Salim, N. (2012). Sentiment classification using graph based word sense disambiguation. AMLTA 2012, CCIS, 322, 351–358.

    Google Scholar 

  15. Xia, Y., Cambria, E., Hussain, A., & Zhao, H. (2014). Word polarity disambiguation using bayesian model and opinion-level features. Cognitive Computation.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Engineering & Applications, GLA University, Mathura, India

    Sonal Garg & Dilip Kumar Sharma

Authors
  1. Sonal Garg
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  2. Dilip Kumar Sharma
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Corresponding author

Correspondence to Sonal Garg .

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Editors and Affiliations

  1. Dept of Comp Sci Engg, Anil Neerukonda Ins Tech & Sci, Visakhapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Sabarkantha, Sabar Institute of Technology, Sabarkantha, Gujarat, India

    Amit Joshi

  3. Studies and Management, Narsinhbhai Institute of Computer, Kadi, Gujarat, India

    Nilesh Modi

  4. Computer Studies and Management, Narsinhbhai Institute of, Kadi, Gujarat, India

    Nisarg Pathak

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© 2016 Springer Science+Business Media Singapore

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Garg, S., Sharma, D.K. (2016). Sentiment Classification of Context Dependent Words. In: Satapathy, S., Joshi, A., Modi, N., Pathak, N. (eds) Proceedings of International Conference on ICT for Sustainable Development. Advances in Intelligent Systems and Computing, vol 408. Springer, Singapore. https://doi.org/10.1007/978-981-10-0129-1_73

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  • DOI: https://doi.org/10.1007/978-981-10-0129-1_73

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0127-7

  • Online ISBN: 978-981-10-0129-1

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