Sentiment-Analysis-on-IMDB-Dataset

Sentiment analysis

• Sentiment analysis is the process of detecting positive or negative sentiment in text. It’s often used by businesses to detect sentiment in social data, gauge brand reputation, and understand customers.

Dataset Used

This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing.

Implementation

The implementation was based on a research paper Sentiment Analysis on Movie Review Data Using Machine Learning Approach.

Classifiers Implemented

1. Multinomial Naive Bayes(MNB)
2. Support Vector Machine(SVM)
3. Maximum Entropy(ME)
4. Decision Tree(DT)
5. Gaussian Naive Bayes(GNB)
6. Convolutional Neural Network (CNN)

Text Representation Models Implemented

1. Bag of Words
2. Word2vec

Accuracy obtained using Bag of Words Model

Classifiers	Accuracy
MNB	85.42%
SVM	83.36%
ME	88.46%
DT	73.40%
GNB	84.60%

Accuracy obtained using Word2vec Model

Classifiers	Accuracy
SVM	84.60%
ME	85.46%
DT	69.04%
CNN	86.19%