A Comparative Study on TF-IDF feature Weighting Method and its Analysis using Unstructured Dataset
Text Classification is the process of categorizing text into the relevant
categories and its algorithms are at the core of many Natural Language
Processing (NLP). Term Frequency-Inverse Document Frequency (TF-IDF) and NLP
are the most highly used information retrieval methods in text classification.
We have investigated and analyzed the feature weighting method for text
classification on unstructured data. The proposed model considered two features
N-Grams and TF-IDF on the IMDB movie reviews and Amazon Alexa reviews dataset
for sentiment analysis. Then we have used the state-of-the-art classifier to
validate the method i.e., Support Vector Machine (SVM), Logistic Regression,
Multinomial Naive Bayes (Multinomial NB), Random Forest, Decision Tree, and
k-nearest neighbors (KNN). From those two feature extractions, a significant
increase in feature extraction with TF-IDF features rather than based on
N-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.