A Study on Sentiment Analysis of Movie Reviews using ML Algorithms |
||
|
|
|
© 2022 by IJCTT Journal | ||
Volume-70 Issue-9 |
||
Year of Publication : 2022 | ||
Authors : Md. Sirajul Huque, V. Kiran Kumar | ||
DOI : 10.14445/22312803/IJCTT-V70I9P104 |
How to Cite?
Md. Sirajul Huque, V. Kiran Kumar, "A Study on Sentiment Analysis of Movie Reviews using ML Algorithms," International Journal of Computer Trends and Technology, vol. 70, no. 9, pp. 33-37, 2022. Crossref, https://doi.org/10.14445/22312803/IJCTT-V70I9P104
Abstract
To understand customer preferences, it is now a routine trend in the modern world to gather opinions and recommendations from individuals using a variety of surveys, polls, and social media platforms. Therefore, an accurate and classical mechanism for making assumptions and anticipating sentiments that can fabricate a positive or negative impact in the market is required to understand the sentiments of customers and their view of the services offered by producers. This type of analysis is important for the relationship between producers and consumers. In order to improve customer satisfaction, the key objective of this paper is to study the recommendations that viewers have left for various movies. This study will be used better to comprehend the mindsets and market behavior of the audience. This study uses two algorithms— Logistic Regression and Naive Bayes to analyze consumer perception of various movies and offers concluding observations.
Keywords
Recommendations, Sentiments, Naive Bayes, Logistic Regression, Perception.
Reference
[1] Shengyi Jiang, Limin Kuang, Meiling Wu, and Guansong Pang, Guangdong University of Foreign Studies, School of Informatics, Guangzhou, China, vol. 39, pp. 1503-1509, 2012.
[2] N. Aston, J. Liddle, and W. Hu, "Twitter Feelings in Data Stream with [J] Perceptron," Journal of Computer and Communications, pp. 11–16, 2014.
[3] “Sentiment Analysis of Hollywood Films on Twitter, by Umesh Rao Hodeghatta,” IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 25–29, 2013.
[4] Bhumika Gupta, Monika Negi, Kanika Vishwakarma, Goldi Rawat, and Priyanka Badhani, “Study of Twitter Sentiment Analysis using Machine Learning Algorithms on Python,” International Journal of Computer Applications, vol. 165, 2017.
[5] B. Liu, “Synthesis Lectures on Human Language Technologies,” Sentiment Analysis and Operation Mining, pp. 152-153, 2016.
[6] P. Nakov Tiedemann, "Combining Word-Level and Character-Level Models for Machine Translation Between Closely Related Languages," Association for Computational Linguistics Meeting: Short Papers, pp. 301-305, 2012.
[7] B. Wen, T. T. He, L. Luo, L. Song, and Q. Wang, "Text Sentiment Classification Research Based on Semantic Comprehension," Computer Science, pp. 261-264. 2010.
[8] “Extraction and Ranking of Product Features,” Lei Zhang University of Illinois, Chicago, Coling 2010 Poster Volume, Beijing, pp. 1462-1470, 2010
[9] A. Khan, B. Baharudin, and K. Khan, "Sentiment Classification from Online Customer Reviews Using Lexical Contextual Sentence Structure," 2nd International Conference on Software Engineering and Computer Systems ICSECS, Springer, pp. 317–331, 2011.
[10] M. Annett and G. Kondrak, “A Comparison of Sentiment Analysis Techniques: Polarizing Movie Blogs,” Canadian Conference on AI, pp. 25–35, 2008.
[11] “Combining Lexical and Learning-Based Techniques for Concept-Level Sentiment Analysis,” ACM, New York, NY, USA, Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining, vol. 5, pp. 1-8, 2012.
[12] Yue Lu, C. Zhai, and H. Wang, “A Rating Regression Method for Latent Aspect Rating Analysis using Review Text Data,” in Knowledge Discovery and Data Mining: Proceedings of the 16th ACM SIGKDD International Conference, ACM, pp. 783–792, 2010.
[13] Fouziah Hamza, S. Maria Celestin Vigila, "A Trust Management Scheme for Intrusion Detection System in MANET using Weighted Naïve Bayes Classifier," International Journal of Engineering Trends and Technology, vol. 70, no. 2, pp. 75-85, 2022 Crossref, https://doi.org/10.14445/22315381/IJCTT-V70I2P211.
[14] A. Kumar, R. Khorwal, and S. Chaudhary, "A Survey on Sentiment Analysis Utilising Swarm Intelligence," Indian Journal of Science and Technology, vol. 9, no. 39, 2016.
[15] X. He et al., "Intelligence Science and Large Data Engineering: Image and Video Data Engineering,” 5th International Conference, IScIDE 2015 Suzhou, China, 2015 Revised Chosen Papers, Part I, Lecture Notes in Computer Science, vol. 9242, no. 1, 2015.
[16] Paramita Ray, "Document Level Sentiment Analysis for Product Review using Dictionary Based Approach," SSRG International Journal of Computer Science and Engineering, vol. 4, no. 6, pp. 24-29, 2017. Crossref, https://doi.org/10.14445/23488387/IJCSE-V4I6P105
[17] Prafulla Mohapatra, Rohit Kumar Singh, Shashank Pandey, Prashanth Anand Kumar, Mrs.Asha K N, "Sentiment Classification of Movie Review and Twitter Data Using Machine Learning," SSRG International Journal of Computer and Organization Trends, vol. 9, no. 3, pp. 1-8, 2019. Crossref, https://doi.org/10.14445/22492593/IJCOT-V9I3P301
[18] N. Muslimah and R. C. Wihandika, “Film Classification Based on Synopsis Using Improved K-Nearest Neighbor (K-NN),” Journal of Information Technology and Computer Science Development, J-PTIIK Universitas Brawijaya, vol. 3, no. 1, pp. 196–204, 2019.
[19] G. Portolese and V. D. Feltrin, “On the Use of Synopsis-based Features for Film Genre Classification,” pp. 892-902, 2019.
[20] J. Wehrmann, M. A. Lopes, and R. C. Barros, “Self-Attention for Synopsis-Based Multi-Label Movie Genre Categorization,” Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS, pp. 236-241, 2018.
[21] D. Bui and J. Doba, “Lyrics Classification Using Naive Bayes,” International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1011–1015, 2018.
[22] A. D. Hartanto, “Job Seeker Profile Classification of Twitter Data using the Nave Bayes Classifier Algorithm Based on the DISC Method,” pp. 533-536, 2019.
[23] G. Portolese and V. D. Feltrin, "On the Use of Synopsis-based Features for Film Genre Classification," pp. 892– 902, 2019.
[24] J. Wehrmann, M. A. Lopes, and R. C. Barros, "Self-Attention for Synopsis-Based Multi-Label Movie Genre Classification," Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS, pp. 236–241, 2018.
[25] N. K. Verma and A. Salour, "Feature Selection," Intelligent Condition Based Monitoring: For Turbines, Compressors, and other Rotating Machines, Springer, Singapore, pp. 175-200, 2020.