Sentiment Analysis Based on Movie Reviews using Various Classification Techniques : A Review

Authors

  • Karishma Kaushik  Department of Computer Science and Engineering and Information Technology, Madhav Institute of Technology and Science, Gwalior India
  • Mahesh Parmar  Department of Computer Science and Engineering and Information Technology, Madhav Institute of Technology and Science, Gwalior India

DOI:

https://doi.org/10.32628/CSEIT217329

Keywords:

Sentiment Analysis, Movie Review, Text Processing, Machine Learning, Feature Extraction, Classification Techniques

Abstract

Sentimental analysis is also called "opinion mining" analyses attitudes and classifies text views. It relates to the use of natural language processing, text, and linguistic processing. A huge amount of data is created with the rapid growth of web technologies. Social networking sites are now popular and normal places where feelings can be shared by short messages. These sentiments involve happiness, sadness, anxiety, fear, etc. The analysis of short texts tends to recognize the crowd's sentiment. Sentiment Analysis on IMDb moviereviews describes a reviewer's general feeling or impression of a movie. Since the perceptions of humans improve the effectiveness of products & since a movie'ssuccess or failure depending on its review, costs are rising, and a good sentiment analysis model needs to be developed, that classifies moviereviews. Machine learning methods use ML algorithms to carry out sentiment analysis as a standard classification problem using syntactic and language characteristics. There are some methods of machine learning used for sentiment analysis in this paper. Most of the sentiment analysis is performed using SVM, RF, ANN, and NB, Algorithms of DT, BN, & KNN.

References

  1. Vishal A. Kharde & S.S. Sonawane,” Sentiment Analysis of Twitter Data: A Survey of Techniques”, International Journal of Computer Applications (0975 – 8887), Volume 139 – No.11, April 2016
  2. N.Mahendran,” A Survey: Sentiment Analysis Using Machine Learning Techniques for Social Media Analytics” International Journal of Pure and Applied Mathematics, Volume 118 No. 8 2018, 419-423
  3. Suraj D. M., Rohan A. R. and Dr. Vimuktha Evangeleen Salis (2019),” Survey on Sentiment Analysis”, International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Vol. 8 Issue 04, April-2019
  4. Pooja Kamavisdar,” A Survey on Image Classification Approaches and Techniques”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 1, January 2013
  5. Suraj D. M., Rohan A. R et al. Survey on Sentiment Analysis,” International Journal of Engineering Research & Technology (IJERT)”, ISSN: 2278-0181, Vol. 8 Issue 04, April-2019
  6. Nirag T. Bhatt,” Sentiment Analysis using Machine Learning Technique: A Literature Survey”, International Research Journal of Engineering and Technology (IRJET), Volume: 07 Issue: 12 | Dec 2020
  7. Vishal A. Kharde,” Sentiment Analysis of Twitter Data: A Survey of Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 139 – No.11, April 2016
  8. Mais Yasen, Sara Tedmori,” Movies Reviews Sentiment Analysis and Classification”, https://www.researchgate.net/publication/332321070_Movies_Reviews_Sentiment_Analysis_and_Classification/link/5cadd038458515cd2b0d62e5/download
  9. H. M. Keerthi Kumar1, B. S. Harish2, H. K. Darshan3,” Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method”, Regular Issue, International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 5, Nº 5
  10. https://www.researchgate.net/publication/330014159_Sentiment_Analysis_on_IMDb_Movie_Reviews_Using_Hybrid_Feature_Extraction_Method/link/5cee44f2299bf109da773a72/download
  11. Shi, S., Cheng, T., Xiao, S., & Lv, X. (2009). Text Processing in Video Frames with Complex Background. 2009 International Forum on Information Technology and Applications. doi:10.1109/ifita.2009.128
  12. Francesco Musumeci, Member, IEEE, Cristina Rottondi, Member, IEEE, Avishek Nag, Member, IEEE, Irene Macaluso, Darko Zibar, Member, IEEE, Marco Ruffini, Senior Member, IEEE, and Massimo Tornatore, Senior Member, IEEE,” An Overview on Application of Machine Learning Techniques in Optical Networks”, arXiv:1803.07976v4 [cs.NI] 1 Dec 2018
  13. V.A. Kanimozhi1 , Dr. T. Karthikeyan2,” A Survey on Machine Learning Algorithms in Data Mining for Prediction of Heart Disease”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, Issue 4, April 2016
  14. RAJ, M. A. 2012. Mrs. Bincy G, Mrs. T. Mathu. Survey on common data mining classification Technique. International Journal of Wisdom Based Computing, 2.
  15. Kesavaraj G, Sukumaran S. A study on classification techniques in data mining. in Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on, 2013; pp. 1-7.
  16. Singh M, Sharma S, Kaur A. Performance Analysis of Decision Trees. International Journal of Computer Applications 2013; 71.
  17. N.Satyanarayana,” Survey of Classification Techniques in Data Mining”, IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 1 Issue 9, November 2014.
  18. N. Chandra Sekhar Reddy, K. Sai Prasad, and A. Mounika,” Classification Algorithms on Datamining: A Study”, International Journal of Computational Intelligence Research, ISSN 0973-1873 Volume 13, Number 8 (2017), pp. 2135-2142
  19. Aized Amin Soof,” Classification Techniques in Machine Learning: Applications and Issues”, Journal of Basic & Applied Sciences, 2017, 13, 459-465
  20. M. Butler and S. Robila, "Interface for querying and data mining for the IMDb dataset," 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT), Farmingdale, NY, USA, 2016, pp. 1-6, doi: 10.1109/LISAT.2016.7494103.
  21. A. Yenter and A. Verma, "Deep CNN-LSTM with combined kernels from multiple branches for IMDb review sentiment analysis," 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), New York, NY, USA, 2017, pp. 540-546, doi: 10.1109/UEMCON.2017.8249013.
  22. Dipak R. Kawade,” Sentiment Analysis: Machine Learning Approach”, International Journal of Engineering and Technology (IJET), Vol 9 No 3 Jun-Jul 2017
  23. A. U. Hassan, J. Hussain, M. Hussain, M. Sadiq and S. Lee, "Sentiment analysis of social networking sites (SNS) data using machine learning approach for the measurement of depression," 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea (South), 2017, pp. 138-140, doi: 10.1109/ICTC.2017.8190959.
  24. S. Mathapati, A. K. Adur, R. Tanuja, S. H. Manjula and K. R. Venugopal, "Collaborative Deep Learning Techniques for Sentiment Analysis on IMDb Dataset," 2018 Tenth International Conference on Advanced Computing (ICoAC), Chennai, India, 2018, pp. 361-366, doi: 10.1109/ICoAC44903.2018.8939068.
  25. M. R. Haque, S. Akter Lima and S. Z. Mishu, "Performance Analysis of Different Neural Networks for Sentiment Analysis on IMDb Movie Reviews," 2019 3rd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE), Rajshahi, Bangladesh, 2019, pp. 161-164, doi: 10.1109/ICECTE48615.2019.9303573.
  26. A. Gupta, A. Singh, I. Pandita and H. Parashar, "Sentiment Analysis of Twitter Posts using Machine Learning Algorithms," 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 2019, pp. 980-983.
  27. P. Karthika, R. Murugeswari and R. Manoranjithem, "Sentiment Analysis of Social Media Network Using Random Forest Algorithm," 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Tamilnadu, India, 2019, pp. 1-5, doi: 10.1109/INCOS45849.2019.8951367.
  28. L. Mandloi and R. Patel, "Twitter Sentiments Analysis Using Machine Learninig Methods," 2020 International Conference for Emerging Technology (INCET), Belgaum, India, 2020, pp. 1-5, doi: 10.1109/INCET49848.2020.9154183.
  29. S. Tripathi, R. Mehrotra, V. Bansal and S. Upadhyay, "Analyzing Sentiment using IMDb Dataset," 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), Bhimtal, India, 2020, pp. 30-33, doi: 10.1109/CICN49253.2020.9242570.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2021-06-30

Issue

Volume 7, Issue 3, May-June-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Karishma Kaushik, Mahesh Parmar, " Sentiment Analysis Based on Movie Reviews using Various Classification Techniques : A Review" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.197-208, May-June-2021. Available at doi : https://doi.org/10.32628/CSEIT217329