🎬 Sentiment Analysis on Movie Reviews

This project performs sentiment analysis on movie reviews using Natural Language Processing (NLP) techniques. The goal is to classify reviews as either positive or negative based on the content.

📜 Table of Contents

Overview Dataset Exploratory Data Analysis (EDA) Preprocessing Modeling Model Evaluation How to Use Results Conclusion

📘 Overview

This project implements a Naive Bayes Classifier to predict the sentiment of movie reviews. It includes:

Data preprocessing (removing stop words, stemming, etc.) Feature extraction using TfidfVectorizer Model training and evaluation Saving and loading models for future use

📊 Dataset

The dataset used is an IMDB movie reviews dataset with two columns:

Review: The text of the movie review. Sentiment: The sentiment associated with the review, labeled as Positive or Negative. The dataset can be found in IMDB.csv.

🔍 Exploratory Data Analysis (EDA)

We perform EDA to understand the dataset:

Shape of the dataset Null values check Distribution of sentiment labels Visualization libraries like Seaborn are used to create count plots to show the distribution of positive and negative reviews.

**🛠️ Preprocessing ** Several preprocessing steps are applied to the raw text data:

Text cleaning: Remove special characters and numbers. Lowercasing: Convert all words to lowercase. Tokenization: Split sentences into individual words. Stopwords removal: Eliminate common but irrelevant words. Stemming: Reduce words to their base form using PorterStemmer. These steps create a cleaned corpus ready for feature extraction.

📐 Modeling

The features from the cleaned text data are extracted using TfidfVectorizer with a maximum feature limit of 5000. The target label is encoded as:

Positive: 1 Negative: 0 We split the dataset into a training set and a test set with an 80/20 ratio and train the model using Multinomial Naive Bayes.

🧪 Model Evaluation

The model is evaluated using:

Accuracy score Confusion matrix Classification report (precision, recall, F1-score)

💻 How to Use

Clone this repository: bash Copy code git clone https://github.com/yourusername/movie-review-sentiment-analysis.git cd movie-review-sentiment-analysis Install the required libraries: bash Copy code pip install -r requirements.txt To train the model, run the notebook or execute: bash Copy code python train.py To use the trained model for sentiment prediction: python Copy code def test_model(sentence): sen = save_cv.transform([sentence]).toarray() res = model.predict(sen)[0] if res == 1: return 'POSITIVE review' else: return 'NEGATIVE review' Example python Copy code sentence = "This is a fantastic movie!" print(test_model(sentence)) # Expected: POSITIVE review

📈 Results

The final model achieved the following performance metrics on the test set:

Accuracy: 85% Precision, Recall, F1-Score (as seen in the classification report)

🚀 Conclusion

This project demonstrates the use of Naive Bayes for text classification in NLP. The trained model performs well on movie reviews, achieving an accuracy of around 85%. It can be extended to other types of textual data by modifying the preprocessing and training steps.

🗂️ Files in the Repository

SENTIMENT_ANALYSIS_ON_MOVIE_REVIEWS.ipynb: Main Jupyter notebook. IMDB.csv: Movie reviews dataset. count-Vectorizer.pkl: Saved TfidfVectorizer model. Movies_Review_Classification.pkl: Trained Naive Bayes classifier model. train.py: Python script to train the model.