# A Study of Feature Extraction techniques for Sentiment Analysis

arXiv: https://arxiv.org/abs/1906.01573 · HTML: https://mlsanity.com/paper/1906.01573/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews](https://mlsanity.com/paper/2107.06802/) (2021)
- [A Comprehensive Review of Visual-Textual Sentiment Analysis from Social Media Networks](https://mlsanity.com/paper/2207.02160/) (2022)

## Abstract (arXiv)

Sentiment Analysis refers to the study of systematically extracting the meaning of subjective text . When analysing sentiments from the subjective text using Machine Learning techniques,feature extraction becomes a significant part. We perform a study on the performance of feature extraction techniques TF-IDF(Term Frequency-Inverse Document Frequency) and Doc2vec (Document to Vector) using Cornell movie review datasets, UCI sentiment labeled datasets, stanford movie review datasets,effectively classifying the text into positive and negative polarities by using various pre-processing methods like eliminating StopWords and Tokenization which increases the performance of sentiment analysis in terms of accuracy and time taken by the classifier.The features obtained after applying feature extraction techniques on the text sentences are trained and tested using the classifiers Logistic Regression,Support Vector Machines,K-Nearest Neighbours , Decision Tree and Bernoulli Nave Bayes