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@article{179741,
        author = {Yashansh Malviya and Dr. Sanjiv Sharma},
        title = {Comparative Analysis of algorithm for classifying sentiments of customers},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8397-8402},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179741},
        abstract = {An crucial part of language processing is 
sentiment analysis, which provides information on 
market trends, consumer satisfaction, and public 
opinion. This study combines a variety of machine 
learning methods, such as BERT, Random Forest, 
Support Vector Machines, Naive Bayes, Logistic 
Regression, and Long Short-Term Memory networks, 
to classify the sentiment of Amazon product reviews. A 
thorough process that includes preprocessing the data, 
TF-IDF feature extraction, and performance 
assessment is used. The results help choose appropriate 
algorithms for sentiment analysis jobs by offering a 
comparative viewpoint on the advantages and 
disadvantages of each model.},
        keywords = {},
        month = {May},
        }