This project focuses on analyzing the sentiment of tweets using various text processing techniques and machine learning algorithms. The goal is to classify tweets as positive, negative, or neutral based on their content.
- Introduction
- Dataset
- Installation
- Usage
- Project Structure
- Model Training
- Evaluation
- Results
- Contributing
- License
Sentiment analysis is a crucial task in natural language processing (NLP) that involves determining the sentiment or emotion expressed in a piece of text. This project leverages tweets to build a sentiment analysis model that can help understand public opinion and trends on social media.
The dataset used in this project contains tweets labeled with their respective sentiment (positive, negative, neutral). It can be found on Kaggle or other open data repositories.
To run this project, you need to have Python installed along with the necessary libraries. You can install the required libraries using the following command:
pip install -r requirements.txt
git clone https://github.com/yourusername/x-tweets-sentiment-analysis.git
cd x-tweets-sentiment-analysis
Model Training
The model training script trains a sentiment analysis model using machine learning algorithms such as Logistic Regression, SVM, or a simple neural network. The script train.py handles the training process.
Evaluation
The evaluation script evaluate.py assesses the performance of the trained model on a test dataset and outputs metrics such as accuracy, precision, recall, and F1-score.
Results
The results of the sentiment analysis model will be displayed in terms of the aforementioned metrics. Visualization of the results can be found in the notebooks/EDA.ipynb notebook.
Contributing
Contributions are welcome! Please fork the repository and submit a pull request for review.
License
This project is licensed under the MIT License. See the LICENSE file for details.
Feel free to modify the content according to your project's specifics and needs.