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Text Classification is the process of categorizing text into the relevantcategories and its algorithms are at the core of many Natural LanguageProcessing (NLP). Term Frequency-Inverse Document Frequency (TF-IDF) and NLPare the most highly used information retrieval methods in text classification.We have investigated and analyzed the feature weighting method for textclassification on unstructured data. The proposed model considered two featuresN-Grams and TF-IDF on the IMDB movie reviews and Amazon Alexa reviews datasetfor sentiment analysis. Then we have used the state-of-the-art classifier tovalidate the method i.e., Support Vector Machine (SVM), Logistic Regression,Multinomial Naive Bayes (Multinomial NB), Random Forest, Decision Tree, andk-nearest neighbors (KNN). From those two feature extractions, a significantincrease in feature extraction with TF-IDF features rather than based onN-Gram. TF-IDF got the maximum accuracy (93.81%), precision (94.20%), recall(93.81%), and F1-score (91.99%) value in Random Forest classifier.

A Comparative Study on TF-IDF feature Weighting Method and its Analysis using Unstructured Dataset