Sentiment Analysis from Comments

This project aims to perform sentiment analysis on user-generated comments using a machine learning approach with the Naive Bayes model. Sentiment analysis is the process of determining the sentiment expressed in a piece of text, such as positive, negative, or neutral. By analyzing comments, this project seeks to gain insights into user opinions and sentiments.

Features

• Data Collection and Preparation : Gathered a dataset of comments or reviews along with their corresponding sentiment labels

• Data Preprocessing : To remove the noice form data

• Feature Extraction : Transformed the textual data into numerical features using techniques such as bag-of-words representation

• Model Training : Utilized the Naive Bayes algorithm, a probabilistic classifier based on Bayes' theorem, to train the sentiment analysis model.

• Model Evaluation : Assessed the performance of the trained model using evaluation metrics such as accuracy, precision

• Model Deployment: Integrated the trained model into an application or system to analyze the sentiment of new comments in real-time

Usage :

(1) Prepare the dataset by gathering comments and their corresponding sentiment labels.

(2) Run the preprocessing script to clean and preprocess the data.

(3) Extract features from the preprocessed data using techniques like TF-IDF .

(4) Train the Naive Bayes model on the extracted features.

(5) Evaluate the model's performance using evaluation metrics.

Contributing

• Contributions are welcome! Please follow these guidelines:

• Fork the repository.

• Create a new branch (git checkout -b feature/fooBar).

• Make your changes and commit them (git commit -am 'Add some fooBar').

• Push to the branch (git push origin feature/fooBar).

• Create a new Pull Request.

Acknowledgments :

• This project was inspired by the need to analyze user sentiments from comments.

Contact :

For inquiries, please contact to email (b210037@nitsikkim.ac.in).