This project demonstrates sentiment analysis using a Naive Bayes classifier to classify text data into positive, negative, or neutral sentiment categories. The classifier is trained on labeled text data and then used to predict the sentiment of new text samples.
- Python
- scikit-learn
- pandas
- Jupyter Notebook
- Git
- Clone the repository:
git clone https://github.com/niladrridas/sentiment-analysis-naive-bayes.git - Navigate to the project directory:
cd sentiment-analysis - Install the required Python packages:
pip install -r requirements.txt
- Open the Jupyter Notebook:
jupyter notebook Sentiment_Analysis.ipynb - Execute the cells in the notebook to load the dataset, preprocess the text data, train the Naive Bayes classifier, and evaluate its performance.
The project demonstrates how to perform sentiment analysis using a simple Naive Bayes classifier. By training the classifier on labeled text data, it can accurately predict the sentiment of new text samples. This approach can be further improved by using more sophisticated models and larger datasets.