This repository contains materials related to Sentiment Analysis of Large Movie Review.

The link for the datatset used are as follows: - https://ai.stanford.edu/~amaas/data/sentiment/

a) utilize both statistical and semantic approaches for sentiment analysis and compare their performance in models;

b) identify the feature representations that work best on the Large Movie Review dataset; and

c) determine the machine learning model that performs best for Sentiment Analysis and explore the impact of optimizing hyperparameters on accuracy and computational efficiency.

1. Data collection
   • type, shape, manipulation
2. Exploratory Data Analysis
   • Data distribution
   • Word count
   • Word length
   • Identified positive and negative words
   • Most occuring word
3. Data Preprocesssing
   • Stop words, special characters, white spaces, punctuation removal
   • Contraction expansion
   • Lowercase transformation
4. Model Training
   • Train-test set split
   • Feature extraction
   • Cross validation
   • Training and testing
   • Prediction
5. Model Evaluation
   • Classification report
   • Accuracy