NLP-based-Sentiment-Analysis-of-IMDb-Movie-Reviews

project for sentiment analysis on IMDb movie reviews using machine learning techniques. The analysis involves cleaning and preprocessing the data, extracting features, training a model, and evaluating its performance.

Project Overview

Data Cleaning:

Removed stopwords and applied lemmatization using spaCy to standardize text.

Feature Extraction:

Used TF-IDF Vectorizer with n-grams (unigrams and bigrams) for better feature representation.

Model Training:

Trained a Logistic Regression model and optimized its hyperparameters using GridSearchCV.

Evaluation:

Evaluated the model with metrics such as accuracy, confusion matrix, and classification report.

Used Cross Validation for robust performance evaluation.

Visualization:

Generated a WordCloud to visualize the most frequent words in the reviews.