Sentiment Analysis with Logistic Regression

This repository contains code for sentiment analysis using Logistic Regression on a movie review dataset. The project includes data loading, preprocessing, feature engineering using TF-IDF vectorization, model training, and performance evaluation.

Overview

Sentiment analysis is a natural language processing task that involves classifying text into predefined categories based on the expressed sentiment. In this project, we use a Logistic Regression model to predict the sentiment of movie review phrases.

Dataset

The dataset used for this project consists of two main files:

• train.tsv: Training data containing movie review phrases with corresponding sentiment labels.
• test.tsv: Test data for evaluating the trained model's performance.

Technologies Used

• Python
• Pandas: Data manipulation and analysis.
• NLTK (Natural Language Toolkit): Text preprocessing, including tokenization, stemming, and stop word removal.
• Scikit-Learn: Machine learning library for TF-IDF vectorization and Logistic Regression model.

Workflow

1. Data Loading and Exploration:

   • Read and explore the dataset using Pandas.
   • Check for missing values and understand the data structure.

2. Text Preprocessing:

   • Tokenize phrases, perform stemming, and remove stop words using NLTK.

3. Feature Engineering:

   • Use TF-IDF (Term Frequency-Inverse Document Frequency) vectorization to convert text data into numerical features.

4. Model Training:

   • Split the dataset into training and validation sets.
   • Train a Logistic Regression model on the TF-IDF transformed features.

5. Model Evaluation:

   • Evaluate the model's performance on both training and validation datasets using accuracy scores.
   • Optionally, visualize a confusion matrix to analyze prediction errors.

Usage

Clone the repository and run the provided Jupyter notebook (sentiment_analysis_logistic_regression.ipynb) to reproduce the sentiment analysis task. Make sure to have Python and the required libraries installed.

git clone https://github.com/pragati9998/Sentimental-Analysis.git
cd Sentimental-Analysis
jupyter notebook sentiment_analysis_logistic_regression.ipynb