Performance Metrics Assessment in Sentimental Analysis over Machine Learning Approaches

This article compares four different classification algorithms based on their performance in determining the sentiment based on the subjective context of the IMDB Movie Review Data. The classification algorithms used are logistic regression, decision tree, support vector machine (SVM), and XGB. Text data pre-processing including the determination of the weight of each word has been done using the Term Frequency Inverse Document Frequency (TFIDF) Vectorizer. Sentiment analysis may also know to be opinion mining, which is the procedure for determining whether the text reveals positive, negative, or neutral sentiment. Through this analysis, business managers can acquire a deep perception of customer opinions about their products. Consumer feedback can influence how well a product performs and the decision to track it can be the difference between a successful product launch and a missed opportunity. By highlighting possibilities to reimagine the user experience, it can also guide marketing and product development. Few metrics measures have been applied to test the efficacy of considered four classifiers and experimented outcome is presented in terms of superior accuracy.