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Introduction to Opinion Mining

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Opinion Mining in Information Retrieval

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

Abstract

The prime component that can enhance the quality of services is the opinion of the users. Factual information and opinion information are two categories of textual information [1]. Facts are sentences which are true and can be verified, whereas opinions are sentences which hold an element of belief and cannot be verified for their truth.

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Notes

  1. 1.

    WordNet is a freely and publically available large lexical dataset of English.

  2. 2.

    SentiWordNet is a lexical resource for opinion mining. SentiWordNet assigns to each synset of WordNet three sentiment scores: positivity, negativity, objectivity (http://SentiWordNet.isti.cnr.it/).

References

  1. Shu, H. (2010). Opinion mining for song lyrics. Thesis.

    Google Scholar 

  2. Sundheim, B. M. (1996). The message understanding conferences. In Proceedings of a workshop on held at Vienna (pp. 35–37). Virginia: Association for Computational Linguistics.

    Google Scholar 

  3. Hu, M., & Liu, B. (2004) Mining opinion features in customer reviews. In AAAI 2004 Jul 25 (Vol. 4, No. 4, pp. 755–760).

    Google Scholar 

  4. Kirchhoff, L. (2010). Applying social network analysis to information retrieval on the World Wide Web: A case study of academic publication space.na.

    Google Scholar 

  5. Jarke, M., & Klamma, R. (2007). Social software und Reflektive Informationssysteme. In Architekturen und Prozesse (pp. 51–62). Berlin: Springer.

    Google Scholar 

  6. Lo, Y. W., & Potdar, V. (2009). A review of opinion mining and sentiment classification framework in social networks. In 2009 3rd IEEE international conference on digital ecosystems and technologies (pp. 396–401). IEEE.

    Google Scholar 

  7. Dave, K., Lawrence, S., & Pennock, D. M. (2003). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th international conference on World Wide Web (pp. 519–528). ACM.

    Google Scholar 

  8. Turney, P. D. (2002). Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting on association for computational linguistics (pp. 417–424). Association for Computational Linguistics.

    Google Scholar 

  9. Wiebe, J. M., Bruce, R. F., & O’Hara, T. P. (1999). Development and use of a gold-standard data set for subjectivity classifications. In Proceedings of the 37th annual meeting of the association for computational linguistics on computational linguistics (pp. 246–253), Association for Computational Linguistics.

    Google Scholar 

  10. Pang, B., Lee, L., & Vaithyanathan, S. (2002, July). Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on empirical methods in natural language processing-Volume 10 (pp. 79–86). Association for Computational Linguistics.

    Google Scholar 

  11. Abulaish, M., Doja, M. N., & Ahmad, T. (2009). Feature and opinion mining for customer review summarization. In Pattern recognition and machine intelligence (pp. 219–224). Berlin: Springer.

    Google Scholar 

  12. Seerat, B., Azam, F. (2012). Opinion mining: Issues and challenges (A survey). International Journal of Computer Applications (0975 – 8887), 49(9).

    Google Scholar 

  13. Khan, K., Baharudin, B., Khan, A., & Ullah, A. (2014). Mining opinion components from unstructured reviews: A review. Journal of King Saud University-Computer and Information Sciences, 26(3), 258–275.

    Article  Google Scholar 

  14. Liu, B. (2008). Opinion mining. Encyclopedia of Database Systems.

    Google Scholar 

  15. Ganesan, K. A., & Kim, H. D. (2008). Opinion mining-A short tutorial (Talk). University of Illinois at Urbana Champaign.

    Google Scholar 

  16. Sharma, N. R., & Chitre, V. D. (2014). Opinion mining, analysis and its challenges. International Journal of Innovations & Advancement in Computer Science, 3(1), 59–65.

    Google Scholar 

  17. Mobasher, B. (2007). Data mining for web personalization. In The adaptive web (pp. 90–135). Berlin: Springer.

    Google Scholar 

  18. Ma, Z. M. (2005). Engineering information modeling in databases: needs and constructions. Industrial Management & Data Systems, 105(7), 900–918.

    Google Scholar 

  19. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1–67.

    Article  Google Scholar 

  20. 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 

  21. Kolkur, S., Dantal, G., & Mahe, R. (2015). Study of different levels for sentiment analysis.

    Google Scholar 

  22. Devi, G. D., & Rasheed, A. A. (2015). A survey on sentiment analysis and opinion mining. International Journal for Research in Emerging Science and Technology, 2(8), 26–31.

    Google Scholar 

  23. Kaur, A., & Duhan, N. (2015). A survey on sentiment analysis and opinion mining. International Journal of Innovations & Advancement in Computer Science, 4, 107–116.

    Google Scholar 

  24. Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems, 28(2), 15–21.

    Google Scholar 

  25. Montoyo, A., MartíNez-Barco, P., & Balahur, A. (2012). Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments.

    Google Scholar 

  26. Osimo, D., & Mureddu, F. (2012). Research challenge on opinion mining and sentiment analysis (p. 508). Bâtiment: Universite de Paris-Sud, Laboratoire LIMSI-CNRS.

    Google Scholar 

  27. Maynard, D., Bontcheva, K., & Rout, D. (2012). Challenges in developing opinion mining tools for social media. In Proceedings of the@ NLP (pp. 15–22).

    Google Scholar 

  28. 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 

  29. 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 

  30. Holmes, G., Donkin, A., & Witten, I. H. (1994). Weka: A machine learning workbench. In Proceedings of the 1994 second Australian and New Zealand intelligent information systems conference (pp. 357–361). IEEE.

    Google Scholar 

  31. 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 

  32. Smeureanu, I., & Bucur, C. (2012). Applying supervised opinion mining techniques on online user reviews. Informaticaeconomica, 16(2), 81.

    Google Scholar 

  33. Bhatia, S., Sharma, M., Bhatia, K. K. (2015). Strategies for mining opinions: A survey. In 2015 2nd international conference computing for sustainable global development (INDIACom) (pp. 262–266).

    Google Scholar 

  34. Bhatia, S., Sharma, M., Bhatia, K. K., & Das, P. (2018). Opinion target extraction with sentiment analysis. International Journal of Computing, 17(3), 136–142. (Elsevier, SCOPUS).

    Google Scholar 

  35. Blinov, P., Klekovkina, M., Kotelnikov, E., & Pestov, O. (2013). Research of lexical approach and machine learning methods for sentiment analysis. Computational Linguistics and Intellectual Technologies, 2(12), 48–58.

    Google Scholar 

  36. Fukumoto, F., & Suzuki, Y. (2000). Event tracking based on domain dependency. In Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval (pp. 57–64). ACM.

    Google Scholar 

  37. Mika, P. (2005). Ontologies are us: A unified model of social networks and semantics. In International semantic web conference (pp. 522–536). Berlin: Springer.

    Google Scholar 

  38. Hu, M., & Liu, B.(2004). Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. ACM.

    Google Scholar 

  39. Miller, G. A. (1995). WordNet: a lexical database for English. Communications of the ACM, 38(11), 39 36b.

    Google Scholar 

  40. Popescu, A. -M., Nguyen, B., & Etzioni, O. (2005). OPINE: Extracting product features and opinions from reviews. In Proceedings of HLT/EMNLP on interactive demonstrations. Association for Computational Linguistics.

    Google Scholar 

  41. Wani, S. S., & Patil, Y. N. (2015, June). Analysis of data retrieval and opinion mining system. In 2015 IEEE international advance computing conference (IACC) (pp. 681–685). IEEE.

    Google Scholar 

  42. Al-Hamami, A. H., & Shahrour, S. H. (2015, December). Development of an opinion blog mining system. In 2015 4th international conference on advanced computer science applications and technologies (ACSAT) (pp. 74–79). IEEE.

    Google Scholar 

  43. Wang, D., Zhu, S., & Li, T. (2013). Sumview: A Web-based engine for summarizing product reviews and customer opinions. Expert Systems with Applications, 40(1), 27–33.

    Article  Google Scholar 

  44. Pisal, S., Singh, J., & Eirinaki, M. (2011, December). AskUs: An opinion search engine. In 2011 IEEE 11th international conference on data mining workshops (ICDMW) (pp. 1243–1246). IEEE; Smolander, J. (2016). Deep learning classification methods for complex disorders.

    Google Scholar 

  45. Vinodhini, G., Chandrasekaran, R. M. (2012). Sentiment analysis and opinion mining: a survey. International Journal, 2(6).

    Google Scholar 

  46. Penalver-Martinez, I., Garcia-Sanchez, F., Valencia-Garcia, R., Rodríguez-García, M. Á., Moreno, V., Fraga, A., et al. (2014). Feature-based opinion mining through ontologies. Expert Systems with Applications, 41(13), 5995–6008.

    Article  Google Scholar 

  47. Song, Q., Ni, J., & Wang, G. (2013). A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Transactions on Knowledge and Data Engineering, 25(1), 1–4.

    Article  Google Scholar 

  48. Dongre, A. G., Dharurkar, S., Nagarkar, S., Shukla, R., Pandita, V. (2016). A survey on aspect based opinion mining from product reviews. International Journal of Innovative Research in Science, Engineering and Technology, 5(2), 2319–8753. ISSN(Online).

    Google Scholar 

  49. Mfenyana, S. I., Moroosi, N., Thinyane, M., & Scott, S. M. (2013). Development of a Facebook crawler for opinion trend monitoring and analysis purposes: case study of government service delivery in Dwesa. International Journal of Computer Applications, 1, 79(17).

    Google Scholar 

  50. Bafna, K., & Toshniwal, D. (2013). Feature based summarization of customers’ reviews of online products. Procedia Computer Science, 22, 142–151.

    Google Scholar 

  51. Varghese, R., Jayasree, M. (2013). Aspect based sentiment analysis using support vector machine classifier. In International conference on advances in computing, communications and informatics (ICACCI), IEEE.

    Google Scholar 

  52. Hemalatha, I., Varma, D. G., & Govardhan, A. (2013). Sentiment analysis tool using machine learning algorithms. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 2(2), 105–109.

    Google Scholar 

  53. McDonald, R., Hannan, K., Neylon, T., Wells, M., Reynar, J. (2007) Structured models for fine-to-coarse sentiment analysis. In Annual meeting-association for computational linguistics 2007 Jun 23 (Vol. 45, No. 1, p. 432).

    Google Scholar 

  54. Dey, N., Babo, R., Ashour, A. S., Bhatnagar, V., & Bouhlel, M. S. (Eds.). (2018). Social networks science: Design, implementation, security, and challenges: From social networks analysis to social networks intelligence. Berlin: Springer.

    Google Scholar 

  55. Singh, A., Dey, N., Ashour, A. S., & Santhi, V. (Eds.). (2017). Web semantics for textual and visual information retrieval. IGI Global.

    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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Correspondence to Surbhi Bhatia .

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Bhatia, S., Chaudhary, P., Dey, N. (2020). Introduction to Opinion Mining. In: Opinion Mining in Information Retrieval. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-15-5043-0_1

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