• Mujthaba Gulam Muqeeth

Abstract: This research examines the effectiveness of machine learning algorithms for sentiment analysis in the area of Natural Language Processing (NLP). The inclusion of machine learning to evaluate sentiment analysis is very applicable due to its prevailing algorithms. These algorithm's performance can be evaluated using traditional methods like Support Vector Machines (SVM), Random Forest, and Logistic regressions leveraging benchmark datasets such as IMDb movie reviews, Twitter sentiment data, and product reviews. The research services a difficult methodology encircling the steps like data collections, data preprocessing, feature extraction, model training, and performance assessment. Evaluation metrics including accuracy, precision, recall, and F1-score are utilized to evaluate algorithm effectiveness, considering diverse feature depictions like Bag-of-Words, TF-IDF, and word embedding’s. Moreover, the research explains possible improvements, including ensemble methods, hyper parameter tuning, and deep learning architectures, to improve sentiment analysis performance. The findings underwrite to consider the aptitudes and limitations of machine learning algorithms in sentiment analysis, focusing valuable insights for selecting appropriate approaches tailored to specific application needs. This research not only advances NLP but also updates the decision-making process in domains needful for sentiment analysis, tiling the way for future innovations and applications in sentiment-aware systems.

Index Terms -NLP, SVM, IMDB, TF-IDF, Sentiment analysis and Evaluation metrics

"Evaluate and Compare the Sentiment Analysis using SVM, Random Forest and Logistic Regression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.e17-e24, July-2024, Available :http://www.jetir.org/papers/JETIR2407403.pdf

"Evaluate and Compare the Sentiment Analysis using SVM, Random Forest and Logistic Regression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppe17-e24, July-2024, Available at : http://www.jetir.org/papers/JETIR2407403.pdf