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

Mamata Das, Selvakumar K and PJA Alphonse

National Institute of Technology Tiruchirappalli, Tamil Nadu, 620015, India

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. This paper mainly investigates and analyzes feature word weight which is used in unstructured data classification of big 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 Naïve 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.

Тэги:

#TF-IDF #N-Gram #Text_classification #Feature_weighting #Information_retrieval

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