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Improving the Performance in Sentiment Analysis

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Authors:
Sumanto Kar, J. Scriptu Rajan, Sebastian Dmello, Sapna Prabhu
10.5120/ijca2021921359

Sumanto Kar, Scriptu J Rajan, Sebastian Dmello and Sapna Prabhu. Improving the Performance in Sentiment Analysis. International Journal of Computer Applications 183(7):19-24, June 2021. BibTeX

@article{10.5120/ijca2021921359,
	author = {Sumanto Kar and J. Scriptu Rajan and Sebastian Dmello and Sapna Prabhu},
	title = {Improving the Performance in Sentiment Analysis},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2021},
	volume = {183},
	number = {7},
	month = {Jun},
	year = {2021},
	issn = {0975-8887},
	pages = {19-24},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume183/number7/31940-2021921359},
	doi = {10.5120/ijca2021921359},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Sentiment Analysis is the contextual mining of text which determines whether the given piece of words is positive, negative, or neutral. The main objective of this work is to make a system that rates movies based on the user's comments made on the movie. The system analyses the data in order to check for user sentiments associated with each comment and gathers all the comments made on a particular movie. It then calculates an average rating in order to score it. The system model checks for sentimental keywords and predicts user sentiment associated with it. Also, the system works on the sarcastic comments in order to find whether the comment is positive or not. Various Python libraries and Django Web Server has been used for the pre-processing of data.

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Keywords

Deep Learning, LSTM Model, Movie Review System, Sentiment Analysis, Sarcasm Classifier, Bagging Algorithm.