Data mining, text mining and opinion mining have occurred in one form or another since modern record keeping began. As the number of online shopping users is increasing, access to social media sites produces vast quantities of information in the form of user feedback, comments, blogs and tweets tests. For this reason, Sentimental analysis is required, which classifies these reviews to gain insights into the data generated by the user. The main problem with the analysis of the feeling is the uncertain mood of the user, such that the interpretation of what the user has written and what he actually thought is somewhat different. The problem analysed in the existing work is that the decision-making trees, particularly when a tree is very large, are likely to parallelize. Random forest classification is used to eliminate both errors due to bias and variance. In the proposed research, the improved technology is implemented with Random forest and optimization of the Ant colony search is hybridised with the proposed classifier in order to accomplish the classification of film screens by studying the sentiments.

Sentiment Analysis, Social Media, Movie Reviews, Data Mining

[1] C. Ouyang, L. Yongbin, Z. Shuqing, and Y. Xiaohua, "Features- level Sentiment Analysis of Movie reviews``, Advance Science and Technology Letters, pp. 110-113, 2016.
[2] S. Kumar Yadav, “Sentiment Analysis and Classification: A Survey”, International Journal of Advance Research in Computer Science and Management Studies, Vol. 3, Issue. 3, 2015.
[3] R. Baldania, “Sentiment analysis approaches for movie reviews forecasting: A survey”, International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017.
[4] A. Tripathi, S. K. Trivedi, “Sentiment analysis of Indian movie review with various feature selection techniques”, IEEE International Conference on Advances in Computer Applications (ICACA), 2016.
[5] C. Catal, M. Nangir, “A Sentiment Classification Model Based On Multiple Classifiers”, Applied Soft Computing Elsevier, vol. 50, pp. 135–141, 2017.
[6] K. Naik, A. Joshi, P. Khanna, N. Shekokar, “A Model to Analyse Social Media Data to Gain a Competitive Edge”, International Conference on Computing, Communication, Control and Automation (ICCUBEA), 2017.
[7] R. Wankhede, A. N. Thakare, “Design approach for accuracy in movies reviews using sentiment analysis”, International Conference of Electronics, Communication and Aerospace Technology (ICECA), 2017.
[8] S. Pandey, S. Sagnika, B. S. P Mishra, “A Technique to Handle Negation in Sentiment Analysis on Movie Reviews”, International Conference on Communication and Signal Processing (ICCSP), 2018.
[9] C. Nanda, M. Dua, G. Nanda, “Sentiment Analysis of Movie Reviews in Hindi Language Using Machine Learning”, International Conference on Communication and Signal Processing (ICCSP)”, 2018.
[10] T. Dholpuria, Y. Rana, C. Agrawal, “A Sentiment analysis approach through deep learning for a movie review”, 8th International Conference on Communication Systems and Network Technologies (CSNT), 2018.
[11] F. Yin, Y. Wang, X. Pan, P. Su, “A Word Vector Based Review Vector method for Sentiment Analysis of Movie Reviews Exploring the Applicability of the Movie Reviews”, 3rd International Conference on Computational Intelligence and Applications (ICCIA), 2018.
[12] O. Hourrane, N. Idrissi, E. H Benlahmar, “Sentiment Classification on Movie Reviews and Twitter: An Experimental Study of Supervised Learning Models”, 1st International Conference on Smart Systems and Data Science (ICSSD), 2019.
[13] M. Yasen, S. Tedmori, “Movies Reviews Sentiment Analysis and Classification”, IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), 2019.
[14] S. Tiwari, A. Verma, P. Garg, D. Bansal, “Social Media Sentiment Analysis on Twitter Datasets”, 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 2020.