  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
