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Sentiment Analysis Through Machine Learning: A Review

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Proceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Sentimental analysis is gaining popularity in the field of text mining. It is the study of people’s opinions about any event, individual, or topic. Users are posting online reviews and opinions about specific products or services and it has become a popular way to share our reviews on the social web, as it is difficult to obtain users' reviews in such a rapid manner through any other means. It also provides us the volume of information on social media like Twitter and Facebook and a range of possible user opinions in a time-saving way. It is difficult as well as interesting due to the bulk amount of information generated by online social media and different kinds of possible opinions. Sentimental analysis on Facebook, Twitter has attracted much attention recently due to its wide applications in various commercial and public sectors. The main focus of this paper is to give a brief overview of sentimental analysis and its techniques and it also provides a comparative analysis of the research done in the field of sentiment analysis. These types of analysis are based on the machine learning approach.

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References

  1. Malo P, Sinha A, Takala P, Ahlgren O, Lappalainen I (2013) Learning the Roles of Directional Expressions and Domain Concepts in Financial News Analysis. In Proceedings of IEEE International Conference on Data Mining Workshops (SENTIRE-2013), IEEE Press

    Google Scholar 

  2. Li S, Zong C (2008) Multi-domain sentiment classification. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers, Association for Computational Linguistics, pp 257–260

    Google Scholar 

  3. Akcora CG, Bayir MA, Demirbas M, Ferhatosmanoglu H (2010) Identifying breakpoints in public opinion.In ACM Proceedings of the First Workshop on Social Media Analytics, July, pp 62–66

    Google Scholar 

  4. Sun E, Rosenn I, Marlow C, Lento T (2009) Gesundheit! modeling contagion through Facebook news feed. In Proc. Of International AAAI Conference on Weblogs and Social Media, May, pp 22

    Google Scholar 

  5. Kwak H, Lee C, Park H, Moon S (2010) What is twitter, a social network or a news media?. In ACM Proceedings of the 19th International Conference on World Wide Web, April, pp 591–600

    Google Scholar 

  6. Cha M, Haddadi H, Benevenuto F, Gummadi KP (2010) Measuring user influence in Twitter: the million follower fallacy. In 4th international AAAI Conference on Weblogs and Social Media (ICWSM), May, vol 14(1)

    Google Scholar 

  7. Yao R, Chen J (2013) Predicting movie sales revenue using online reviews. In Proceedings - 2013 IEEE International Conference on Granular Computing, GrC, pp 396–401. https://doi.org/10.1109/GrC.2013.6740443

  8. Weng J, Lim EP, Jiang J, He Q (2010) Twitter rank: finding topic sensitive influential twitterers. In Proceedings of the Third ACM International Conference on Web Search and Data Mining, February, pp 261–270

    Google Scholar 

  9. Mullen, Tony, Nigel Collier (2004) Sentiment Analysis using SVM with Diverse Information Sources. EMNLP vol 4

    Google Scholar 

  10. Cheong M, Lee VCS (2011) A micro blogging-based approach to terrorism informatics: Exploration and chronicling civilian sentiment and response to terrorism events via Twitter. Inf Syst Front 13(1):45–59

    Article  Google Scholar 

  11. Yates D, Paquette S (2011) Emergency knowledge management and social media technologies: a case study of the 2010 Haitian earthquake. Int J Inf Manage 31(1):6–13

    Article  Google Scholar 

  12. Lee NY, Kim Y, Sang Y (2017) How do journalists leverage Twitter? Expressive and consumptive use of Twitter. Social Sci. J. 54(2):139–147

    Article  Google Scholar 

  13. Crannell WC, Clark E, Jones C, James TA, Moore J (2016) A pattern-matched twitter analysis of US cancer-patient sentiments. J Surg Res 206(2):536–542

    Article  Google Scholar 

  14. Carley KM, Malik M, Landwehr PM, Pfeffer J, Kowalchuck M (2016) Crowd sourcing disaster management: the complex nature of Twitter usage in Padang Indonesia. Saf Sci 90:48–61

    Article  Google Scholar 

  15. Nagamma P, Pruthvi HR, Nisha KK, Carlos Soares (2015) An Improved Sentiment Analysis of Online Movie Reviews. IEEE, International conference on Computer and Information Technology.

    Google Scholar 

  16. Ahmed S, Jaidka K, Cho J (2016) The 2014 Indian elections on Twitter: a comparison of campaign strategies of political parties. Telematics Inform 33(4):1071–1087

    Article  Google Scholar 

  17. Vinodhini G, Chandrasekaran RM (2012) Sentiment Analysis and Opinion Mining: A Survey vol 2, Issue 6, June 2012, IEEE paper

    Google Scholar 

  18. Popescu AM, Etzioni O (2005) Extracting Product Features and Opinions from Reviews. In Proc. Conf. Human Language Technology and Empirical Methods in Natural Language Processing, Vancouver, British Columbia, pp 339–346

    Google Scholar 

  19. Hu, Liu (2006) Opinion extraction and summarization on the web. AAAI, pp 1621–1624

    Google Scholar 

  20. http://www.cs.cornell.edu/People/pabo/movie-reviewdata.

  21. http://www.cs.jhu.edu/mdredze/datasets/sentiment

  22. Govindarajan M (2013) Sentiment Analysis of Movie Reviews using Hybrid Method of Naive Bayes and Genetic Algorithm. Int. J. Adv. Comput. Res 3(4) Issue-13

    Google Scholar 

  23. Ozturk N, Ayvaz S (2018) Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis. Telematics Inform 35(1):136–147

    Google Scholar 

  24. Hodeghatta, Umesh Rao (2013) Sentiment Analysis of Hollywood Movies on Twitter. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, August 2013, pp 1401–1404

    Google Scholar 

  25. Narendra B, Uday Sai K, Rajesh G, Hemanth K, Chaitanya Teja MV, Deva Kumar K (2016) Sentiment Analysis on Movie Reviews: A Comparative Study of Machine Learning Algorithms and Open Source Technologies. Int. J. Intell. Syst. Appl. (IJISA) 8(8): 66–70. https://doi.org/10.5815/ijisa.2016.08.08.

  26. Shamantha RB, Shetty SM, Rai P (2019) Sentiment Analysis Using Machine Learning Classifiers: Evaluation of Performance. IEEE 4th International Conference on Computer and Communication Systems (ICCCS), https://doi:https://doi.org/10.1109/ccoms.2019.8821650.

  27. Vakili M, Ghamsari M, Rezaei M (2020) Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification. arXiv preprint arXiv:2001.09636.

  28. Melville, Wojciech G, (2009) Sentiment analysis of blogs by combining lexical knowledge with text classification KDD 09, Paris, France. 978-1-60558-495-9/09/06.

    Google Scholar 

  29. Chen LS, Liu CH, Chiu HJ (2011) A neural network based approach for sentiment classification in the blogosphere. J Informetrics 5: 313–322

    Google Scholar 

  30. Xia R, Zong C, Li S (2011) Ensemble of feature sets and classification algorithms for sentiment classification. Inf Sci 181:1138–1152

    Google Scholar 

  31. Rathor AS, Agarwal A, Dimri P (2018) Comparative study of machine learning approaches for amazon reviews. Procedia Comput Sci 132:1552–1561. https://doi.org/10.1016/j.procs.2018.05.119

  32. Moraes R, Valiati JF, Neto WP (2013) Document-level sentiment classification: an empirical comparison between SVM and ANN. Expert Syst Appli 40: 621–633. https://doi.org/10.1016/j.eswa.2012.07.059

  33. Li G, Liu F (2010) A clustering-based approach on sentiment analysis, 978-1-4244-6793-8/10 ©2010 IEEE

    Google Scholar 

  34. Xu K, Liao SS, Li J, Song Y (2011) Mining comparative opinions from customer reviews for competitive intelligence. Decis Support Syst 50:743–754

    Google Scholar 

  35. Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. ACL

    Google Scholar 

  36. Somprasertsri G, Lalitrojwong P (2010) Mining feature-opinion in online customer reviews for opinion summarization. J Univers Comput Sci, 16(6):938–955

    Google Scholar 

  37. Kaur G, Kaur K (2017) Sentiment detection from Punjabi text using support vector machine. Int J Sci Res Comput Sci Eng 5:39-46. https://doi.org/10.26438/ijsrcse/v5i6.3946

  38. Amolik A, Jivane N, Bhandari M, Dr Venkatesan (2016) Twitter sentiment analysis of movie. Int J Eng Technol (IJET) 7(6)

    Google Scholar 

  39. Kennedy A, Inkpen D (2006) Sentiment classification of movie reviews using contextual valence shifters. Comput Intell 22:110–125

    Google Scholar 

  40. Wankhede R, Thakare AN (2017) Design approach for accuracy in movies reviews using sentiment analysis. In: International conference of Electronics Communication and Aerospace Technology (ICECA), 6-11. https://doi.org/10.1109/ICECA.2017.8203652

  41. Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. EMNLP. 10.10.3115/1118693.1118704

    Google Scholar 

  42. Prabowo R, Thelwall M (2009) Sentiment analysis: a combined approach. J Informetrics 3:143–157

    Google Scholar 

  43. Gamon M (2004) Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In Proceedings of the 20th international conference on Computational Linguistics (COLING ’04). Association for Computational Linguistics, USA, 841–es

    Google Scholar 

  44. Dave K, Lawrence S, Pennock DM (2003) Mining the peanut gallery: opinion extraction and semantic classification of product reviews. Proceedings of WWW03, 12th International Conference on the World Wide Web, pp 519–528. Budapest, HU: ACM Press

    Google Scholar 

  45. König AC, Brill ED (2006) Reducing the human overhead in text categorization. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '06). Association for Computing Machinery, New York, NY, USA, 598–603

    Google Scholar 

  46. Nagamma P, Pruthvi HR, Nisha KK, Shwetha NH (2015) An improved sentiment analysis of online movie reviews based on clustering for box-office prediction. International Conference on Computing, Communication and Automation, ICCCA 2015. 933–937

    Google Scholar 

  47. Thorat AM, Priya RV (2018) Sentiment analysis of movie review using text mining. Int J Pure Appl Math, 119(16)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science & Engineering, I.K. Gujral Punjab Technical University, Jalandhar, Punjab, India

    Meenu Bhagat & Brijesh Bakariya

Authors
  1. Meenu Bhagat
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  2. Brijesh Bakariya
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Editor information

Editors and Affiliations

  1. Poornima College of Engineering, Jaipur, India

    Garima Mathur

  2. Poornima College of Engineering, Jaipur, India

    Mahesh Bundele

  3. Rajasthan Technical University, Kota, Rajasthan, India

    Mahendra Lalwani

  4. Polish Academy of Sciences, Warsaw, Poland

    Marcin Paprzycki

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Bhagat, M., Bakariya, B. (2022). Sentiment Analysis Through Machine Learning: A Review. In: Mathur, G., Bundele, M., Lalwani, M., Paprzycki, M. (eds) Proceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6332-1_52

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