This repository focuses on the customers sentiment analysis tweets from Twitter using supervising and unsupervising techniques. The src directory contains the library with code used in these notebooks to create and clean text features found in the sub-directory 'processing' and the 'visualization' sub-directory contain the code to visualize the categorical correlation.
- Learn how to quickly connect with your customers to create a positive brand reputation by deploying a customer sentiment model with a supervised and unsupervised classification model
- Leverage natural language processing (NLP) to extract insights from text features and learn how to handle the unstructured text to numerical embeddings
- Clone the repository.
- cd anomaliesinamazonreviews
- Create a virtual environment and install all dependencies. More here.
Navigate to the Jupyter notebooks and run the notebook:
- jupyter notebook
- Click on notebooks
- Both baseline and proposed notebooks
- Visit our website at https://www.phdata.io/ or send us an email at info@phdata.io