Abstract
Sentiment analysis, a critical topic in Natural Language Processing (NLP). Classification of reviews into categories that are neutral, positive, or negative is the primary goal. This study employed the dataset public available called as Amazon reviews dataset. Study investigated different aspects taken from a representation of the text data in the form of a bag of words. We have experimented with feature selection methods, particularly Term Frequency-Inverse Document Frequency (TFIDF), to improve the model's performance. Then, for the classification task, a Support Vector Machine (SVM) classifier was used. The outcomes showed that the model's performance was enhanced by combining TFIDF feature selection with bag of words feature extraction. This method of work improved the accuracy of classification models, which helps NLP applications forecast various review more accurately. Key Words: NLP, Bag of Words, TFIDF, SVM, Amazon Reviews Dataset, Machine Learning.
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