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

arXiv: https://arxiv.org/abs/2308.04037 · HTML: https://mlsanity.com/paper/2308.04037/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Builds on (most cited first, 1 of 1)

- [Detecting Hate Speech and Offensive Language on Twitter using Machine Learning: An N-gram and TFIDF based Approach](https://mlsanity.com/paper/1809.08651/) (2018)

## Built on by (most cited first, 10 of 10)

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- [How good are LLMs at Retrieving Documents in a Specific Domain?](https://mlsanity.com/paper/2509.22658/) (2025)
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## Abstract (arXiv)

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.