IMDb Movie Data Classification using Voting Classifier for Sentiment Analysis
DOI:
https://doi.org/10.26438/ijcse/v10i1.1823Keywords:
Sentiment Analysis, Feature Extraction, Voting classifier, Machine Learning, IMDb dataAbstract
Social networking sites have become popular and common places in which short texts share emotional diversity. These emotions are sadness, happiness, fear, anxiety, and so on. In order to identify sentiments expressed by the crowd, it helps in analyzing short texts. On IMDb movie reviews, sentiment analysis identifies a reviewer's overall sentiment or opinion on a movie. We worked on the IMDb movie dataset in this paper. which was retrieved from Kaggle which was crawled and labelled positive/negative. The available dataset consists of emoticons, Id, Data, Query, username and converted into a standard from. We get these results by utilizing a Voting Classifier with Logistic Regression & Random Forest, which is a traditional machine learning algorithm. Furthermore, the results of these algorithms were compared using five evaluation criteria. metrics – accuracy(89.34), precision(88.71), recall(90.35), F1 measure(89.52), and Area under Curve (89.33).
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