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.
- The model is trained on the Twitter Entity Sentiment Analysis dataset.
- The dataset consists of Twitter data with labeled sentiments (positive, negative, neutral).
- 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.
To use the Twitter Sentiment Analysis Model, follow these steps:
- Download the Twitter Entity Sentiment Analysis dataset.
- Place the dataset file in the same directory as the Jupyter Notebook file.
- Open the Jupyter Notebook file in this repository.
- Run the notebook to train the model and make predictions on new Twitter data.
- Python (version 3.6 or higher)
- scikit-learn (version 0.24 or higher)
- Jupyter Notebook
- The results can be viewed and analyzed within the Jupyter Notebook.
This project is licensed under the MIT License. See the LICENSE file for more details.
- 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.