A tool that analyzes the overall sentiment of customer reviews for a specific product or service, whether it's positive or negative. This analysis is performed by using natural language processing algorithms and machine learning from the model Reviews-Sentiment-Analysis trained by Kaludi, allowing businesses to gain valuable insights into customer satisfaction and improve their products and services accordingly.
This tool is built using the Gradio library and utilizes the transformers library for its machine learning capabilities.
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The sentiment analysis tool uses a pre-trained model 'Reviews-Sentiment-Analysis' available on HuggingFace at https://huggingface.co/Kaludi/Reviews-Sentiment-Analysis.
The 'Reviews-Sentiment-Analysis' model was trained on a dataset of customer reviews also available on HuggingFace at https://huggingface.co/datasets/Kaludi/data-reviews-sentiment-analysis.
- Clone or download the repository.
- Install the required libraries by running
pip install -r requirements.txt. - Run the script using
python app.py. - Input a customer review in the textbox and click on "Run".
- The output will show the sentiment prediction of the review as either Positive or Negative along with the respective confidence score.
- Gradio
- Transformers
- Numpy
- Pandas
- Pickle
- Scipy
The model Reviews-Sentiment-Analysis was trained by Kaludi and is available on HuggingFace.