Movies Reviews Sentiment Analysis using Improved Random Forest Algorithm and ACO (Ant Colony Optimization) Approach

Authors

  • Deol N.K Dept. of Information Technology, Guru Nanak Dev Engineering college, Ludhiana, India
  • Thapar V Dept. of Computer Science and Engineering, Guru Nanak Dev Engineering college, Ludhiana, India
  • Singh j Dept. of Computer Science and Engineering, Guru Nanak Dev Engineering college, Ludhiana, India

DOI:

https://doi.org/10.26438/ijcse/v9i9.2530

Keywords:

Sentiment Analysis, Social Media, Movie Reviews, Data Mining

Abstract

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.

References

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Published

2021-09-30
CITATION
DOI: 10.26438/ijcse/v9i9.2530
Published: 2021-09-30

How to Cite

[1]
N. Deol, V. Thapar, and J. Singh, “Movies Reviews Sentiment Analysis using Improved Random Forest Algorithm and ACO (Ant Colony Optimization) Approach”, Int. J. Comp. Sci. Eng., vol. 9, no. 9, pp. 25–30, Sep. 2021.

Issue

Vol. 9 No. 9 (2021): IJCSE September Edition

Section

Research Article

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