Sentiment Analysis on Twitter Data

Overview

This project focuses on performing sentiment analysis on Twitter data. It involves tasks such as data cleaning, preprocessing, and building a machine learning model to classify sentiments as positive or negative.

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Requirements

The following tools and libraries are required to run this project:

• Python 3.x
• pandas
• numpy
• scikit-learn
• nltk

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Installation

1. Clone the repository:

   git clone https://github.com/YourUsername/sentiment_analysis_on_twitter_data.git

2. Navigate to the project directory:

   cd sentiment_analysis_on_twitter_data

3. Install the required Python packages:

   pip install -r requirements.txt

4. Download necessary NLTK resources (stopwords, punkt):

   import nltk
   nltk.download('stopwords')
   nltk.download('punkt')

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Usage

1. Prepare your dataset:

   • Place your dataset in the project directory and rename it to twitter_data.csv.

2. Run the preprocessing script:

   • Clean and preprocess the text data for model training.

3. Split the data:

   • Split the data into training and testing sets.

4. Train the model:

   • Build and train a machine learning model using Logistic Regression or any other classifier.

5. Evaluate the model:

   • Test the model on unseen data and calculate accuracy metrics.

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License

This project is licensed under the MIT License - see the LICENSE file for details.