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Opinion Mining in Information Retrieval

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSINTELL))

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Abstract

This book focuses on developing an overall system for summarizing opinions based on aspects. There are mainly three pieces of work.

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References

  1. Navigli, R., & Velardi, P. (2004). Learning domain ontologies from document warehouses and dedicated web sites. Computational Linguistics, 30(2), 151–179.

    Article  Google Scholar 

  2. Huang, E. H., Socher, R., Manning, C. D., & Ng, A. Y. (2012). MaxMax: A graph-based soft clustering algorithm applied to word sense induction. In Proceedings of the 14th International Conference on Computational Linguistics and Intelligent Text Processing (pp. 368–381).

    Google Scholar 

  3. Moro, A., Raganato, A., & Navigli, R. (2014). Entity linking meets word sense disambiguation: A unified approach. Transactions of the Association for Computational Linguistics, 2, 231–244.

    Article  Google Scholar 

  4. Maynard, D., Bontcheva, K., & Rout, D. (2012). Challenges in developing opinion mining tools for social media. Proceedings of the@ NLP can u tag# usergeneratedcontent (pp. 15–22).

    Google Scholar 

  5. Bhatia, M., Sharma, S., Bhatia, S., & Alojail, M., (2020). Fog computing mitigate limitations of cloud computing. International Journal of Recent Technology and Engineering (IJRTE), SCOPUS.

    Google Scholar 

  6. Gruhl, D., Chavet, L., Gibson, D., Meyer, J., Pattanayak, P., Tomkins, A., & Zien, J. (2004). How to build a WebFountain: An architecture for very large-scale text analytics. IBM Systems Journal, 43(1), 64–77.

    Google Scholar 

  7. Scaffidi, C., Bierhoff, K., Chang, E., Felker, M., Ng, H., & Jin, C. (2007). Red opal: Product-feature scoring from reviews. In Proceedings of the 8th ACM Conference on Electronic Commerce (pp. 182–191). ACM.

    Google Scholar 

  8. Bhatia, S., & Madaan, R. (2020). Understanding the role of emotional intelligence in usage of social media. In Confluence 2020, Amity University. IEEE.

    Google Scholar 

  9. Holmes, G., Donkin, A., & Witten, I. H. (1994). Weka: A machine learning workbench. In Intelligent Information Systems, 1994. Proceedings of the 1994 Second Australian and New Zealand Conference (pp. 357–361). IEEE.

    Google Scholar 

  10. Chatterjee, S., Datta, B., Sen, S., Dey, N., & Debnath, N. C. (2018, January) Rainfall prediction using hybrid neural network approach. In 2018 2nd International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom) (pp. 67–72). IEEE.

    Google Scholar 

  11. Lin, C. Y. (2004). Rouge: A package for automatic evaluation of summaries. In Text summarization Branches out: Proceedings of the ACL-04 Workshop (Vol. 8).

    Google Scholar 

  12. Kaur, J., & Saini, J. R. (2015). A study of text classification natural language processing algorithms for Indian languages. The VNSGU Journal of Science Technology, 4(1), 162–167.

    Google Scholar 

  13. Blitzer, J., Dredze, M., & Pereira, F. (2007). Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In ACL (Vol. 7, pp. 440–447).

    Google Scholar 

  14. Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics (Vol. 10 pp. 79–86).

    Google Scholar 

  15. Ganesan, K. A., Zhai, C. X., & Han, J. (2010). Opinosis: A graph based approach to abstractive summarization of highly redundant opinions. In Proceedings of the 23rd International Conference on Computational Linguistics (COLING ‘10).

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Systems, King Faisal University, Al Hasa, Saudi Arabia

    Surbhi Bhatia

  2. Department of Computer Science and Engineering and Information Technology, The NorthCap University, Gurugram, India

    Poonam Chaudhary

  3. Department of Information Technology, Techno India College of Technology, Kolkata, India

    Nilanjan Dey

Authors
  1. Surbhi Bhatia
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  2. Poonam Chaudhary
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  3. Nilanjan Dey
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Corresponding author

Correspondence to Surbhi Bhatia .

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Bhatia, S., Chaudhary, P., Dey, N. (2020). Conclusions. In: Opinion Mining in Information Retrieval. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-15-5043-0_7

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