sentiment-analysis

Project Structure

• Sentiment_Analysis_Tool.ipynb: The Jupyter notebook that contains the complete code to build and test the sentiment analysis model.
• README.md: This file, containing the project description and instructions.

Steps Included

1. Data Preprocessing: Cleansing of the text data, including removal of stop words and punctuation.
2. Feature Extraction: Converting text data into numerical form using CountVectorizer.
3. Model Building: Training a Naive Bayes classifier to predict the sentiment of customer feedback.
4. Model Evaluation: Assessing the model's performance using accuracy and confusion matrix.
5. Testing: Predicting sentiment for new customer feedback.

How to Use

1. Clone this repository to your local machine.
2. Open the Sentiment_Analysis_Tool.ipynb in Jupyter Notebook or JupyterLab.
3. Run each cell step-by-step to execute the code and build the model.
4. You can modify the sample feedback data or input new customer feedback to test the tool.

Example

Here’s how you can test the model with new feedback:

new_feedback = ["The product is great and I love it!"]
cleaned_new_feedback = [clean_text(feedback) for feedback in new_feedback]
new_feedback_vector = vectorizer.transform(cleaned_new_feedback)

# Predict sentiment
predicted_sentiment = nb.predict(new_feedback_vector)
print(f"Predicted sentiment: {predicted_sentiment[0]}")

Built With

• jupyter-notebook