A study of feature extraction techniques for sentiment analysis

Abstract

Sentiment analysis refers to the study of systematically extracting the meaning of subjective text. When analyzing 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 preprocessing methods like eliminating stop words 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 neighbors, decision tree, and Bernoulli Naive Bayes.