A Review of: Ensemble Feature Selection Scheme-Based Performance Evaluation of Several Classifiers for Sentiment Analysis
Anupriya Singh · International Journal for Research in Applied Science and Engineering Technology · 2024
Abstract: The rise in popularity of sentiment analysis can be attributed to the growing amount of user-generated content available on the internet in recent times. Robust machine learning models and effective feature selection procedures are necessary for effectively extracting sentiment from textual data. This paper provides a thorough examination of the evaluation of several classifiers' performances in sentiment analysis using an ensemble feature selection scheme. The suggested ensemble feature selection methodology aims to improve the overall efficacy of sentiment analysis models by combining the best aspects of several feature selection techniques. To find the most pertinent features for sentiment classification, feature selection techniques like information gain, chi-square, and recursive feature reduction are combined into an ensemble framework. By reducing the drawbacks of individual feature selection methods, the ensemble approach yields a feature subset that is more extensive. Multiple state-of-the-art classifiers, including as support vector machines, decision trees, and neural networks, are used to assess the efficacy of the ensemble feature selection approach. Benchmark sentiment analysis datasets covering a wide range of subjects and linguistic subtleties are used to train and evaluate the classifiers. The outcomes of the experiment show that the ensemble feature selection strategy greatly enhances sentiment analysis model performance for a variety of classifiers. Comparative evaluations highlight the advantages and disadvantages of each classifier in different scenarios, providing insight into how wellsuited they are for sentiment analysis jobs in the real world. The research investigates how ensemble size and diversity affect overall performance, providing information about the ideal setup for sentiment analysis applications. The results of this study provide a methodical assessment of classifier performance in combination with an ensemble feature selection approach, which advances sentiment analysis techniques. This research offers a new method for sentiment analysis that combines several classifiers with an ensemble feature selection methodology. The findings emphasize how crucial it is to choose the right features for sentiment classification tasks and show how ensemble approaches can improve overall model performance.