Twitter Sentiment Analysis Model

This repository contains a Jupyter Notebook file that implements a machine learning model for sentiment analysis on Twitter data. The model is built using logistic regression and Python.

Dataset

• The model is trained on the Twitter Entity Sentiment Analysis dataset.
• The dataset consists of Twitter data with labeled sentiments (positive, negative, neutral).

Model

• The model is based on a supervised learning algorithm called logistic regression.
• It uses a linear classifier to predict the sentiment of a given Twitter text based on its features.

Usage

To use the Twitter Sentiment Analysis Model, follow these steps:

1. Download the Twitter Entity Sentiment Analysis dataset.
2. Place the dataset file in the same directory as the Jupyter Notebook file.
3. Open the Jupyter Notebook file in this repository.
4. Run the notebook to train the model and make predictions on new Twitter data.

Requirements

• Python (version 3.6 or higher)
• scikit-learn (version 0.24 or higher)
• Jupyter Notebook

Results

• The results can be viewed and analyzed within the Jupyter Notebook.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

• The Twitter Entity Sentiment Analysis dataset is provided by Kaggle.
• The model implementation and techniques are inspired by various resources and tutorials in the field of natural language processing and sentiment analysis.

For more details and a step-by-step guide, refer to the Jupyter Notebook file in this repository.