Abstract
AbstractAmong the most essential NLP applications is sentiment analysis, often known as opinion mining. In recent years, sentiment analysis has received a lot of attention. It is concerned with text classification in order to establish the intent of the text’s final user. End-user feedback becomes the most important factor in determining the quality of a product’s content. Online product reviews are regarded as one of the most important sources of customer feedback. In the current environment, people may discover on products and make selections based on online review sites. Hence, extracting the precise product reviews from the dataset wherein we have preprocessed the dataset using sentimental analysis, counter vectorizer is used for feature extraction in bag of words and the classification is done through SVM, KNN, RF, DT, NB to predict the review status whether it is positive, negative, or neutral. The model is tuned with grid search, and the confusion matrix is plotted for the above-mentioned algorithms. The final aim of the work is to provide users with their wanted products. Thus, we have tested our categorization approach through amazon reviews.KeywordsNLPSentimental analysisOpinion miningCounter vectorizerFeature extractionGrid searchAmazon reviews
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