Abstract:
Movie reviews assist viewers in determining whether a film is worth their time. Sentiment
analysis is the procedure of investigating digital text to calculate whether the emotional
tone of a word is favorable, unfavorable, or neutral. In the proposed study we Used IMDb
Dataset., Because IMDb one of the most well-known internet databases for movies and
people. This gives users access to a huge and varied dataset for sentiment analysis. and
make the data overwhelming numerous measures such as word clouds and text stemming
methods. Natural language processing (NLP) takes employed toward develop the
suggested prototype because movie comments lack grammatical structures, and
experiments have been conducted to come up to the current investigation with already existing learning model. We also applied some machine learning classifiers such as
Logistics Regression (LR), Multinomial Naïve Bayes (MNB), Support Vector Classifiers
(SVC), Decision Tree (DT), and Random Forest (RF). In addition, the proposed approaches
are 5-fold cross-validation to obtain the accuracy rate as well as hyperparameter tuning in
separate classifiers to allocate the finest parameters. The applied approaches presentation
was assessed to regulate “Accuracy”, “Precision”, “Recall” and “f-score”. at what time all
methods were likened, “Support Vector Classifier” gives uppermost correctness of 89.41%
