The Sentiment Analysis for Product Rating project is a machine learning-based application that aims to analyze and predict sentiment polarity (positive, negative, or neutral) from product reviews. By utilizing natural language processing techniques and sentiment analysis algorithms, this project provides insights into the sentiment expressed in textual product reviews.
- Sentiment analysis for product reviews: The application analyzes the sentiment expressed in product reviews and predicts whether the sentiment is positive, negative, or neutral.
- Pretrained model integration: The project utilizes a pretrained sentiment analysis model to classify sentiments accurately.
- Customization: The system allows customization and fine-tuning of the sentiment analysis model according to specific requirements.
- Data visualization: The project includes visualizations and statistical summaries of sentiment analysis results, providing valuable insights into the sentiment distribution of product reviews.
- Python: The core programming language used for development.
- Natural Language Processing (NLP) libraries: Used for text preprocessing, feature extraction, and sentiment analysis (e.g., NLTK, spaCy).
- Machine Learning libraries: Utilized for model training and sentiment classification (e.g., scikit-learn, TensorFlow, PyTorch).
- Data visualization libraries: Employed for generating visualizations and statistical summaries (e.g., Matplotlib, Seaborn).
To use this project, perform the following steps:
- Clone this repository:
git clone https://github.com/CodeWreckPro/Sentiment_analysis_for_product_rating.git - Navigate to the project directory:
cd Sentiment_analysis_for_product_rating - Install the required dependencies:
pip install -r requirements.txt
Follow the instructions below to run the project:
- Prepare your product review dataset in a compatible format (e.g., CSV, JSON, TXT).
- Preprocess the data by cleaning, tokenizing, and transforming it into a suitable format for the sentiment analysis model.
- Train the sentiment analysis model using the prepared dataset or load a pretrained model if available.
- Analyze new product reviews by utilizing the trained model or pretrained model.
- Visualize the sentiment analysis results to gain insights into the sentiment distribution.
Detailed instructions and code examples can be found in the project's documentation here.
To train and evaluate the sentiment analysis model, a labeled product review dataset is required. The dataset should include reviews along with corresponding sentiment labels (positive, negative, or neutral). Ensure that the dataset is properly formatted and compatible with the project's data preprocessing functions.
Contributions are welcome! If you'd like to contribute to this project, please follow these steps:
- Fork the repository.
- Create a new branch for your feature:
git checkout -b feature-name. - Make the necessary changes and commit them:
git commit -m 'Add some feature'. - Push your changes to the branch:
git push origin feature-name. - Open a pull request in the original repository.
This project is licensed under the MIT License.
If you have any questions, suggestions, or feedback, please feel free to contact the project maintainer:
- Name: Rakshith S
- Email: [email protected]
Thank you for using the Sentiment Analysis for Product Rating project!