This repository contains the code and data for our thesis project titled "DEEP NLP AND DISTILBERT CONVERSATIONAL CHATBOT FOR CUSTOMER RELATIONSHIP MANAGEMENT". The project aims to improve the communication capability of a chatbot in understanding the subtleties and difficulties of the human language by using the Enhanced Transformer-Based model (DistilBERT) with the NLP-based chatbot machine for better performances in customer services applicable in the e-commerce business industries.
Follow these steps to set up the project environment:
Download the Python installer here: https://www.python.org/downloads/release/python-3121/. The user must have a personal computer or a laptop to install the Python 3 interpreter. If you select “Install Now”:
- You will not need to be an administrator (unless a system update for the C Runtime Library is required or you install the Python Launcher for Windows for all users).
- Python will be installed into your user directory.
- The Python Launcher for Windows will be installed according to the option at the bottom of the first page.
- The standard library, test suite, launcher, and pip will be installed.
- If selected, the install directory will be added to your PATH.
- Shortcuts will only be visible to the current user.
Clone the repository.
To run the code and reproduce the results, you will need to download the prerequisite libraries used.
- List of Libraries
- Open the terminal and use the command:
pip install -r requires.txtThis will reinstall all modules and also has the benefit of downloading the correct versions for the destination device hard/software.
Run the application "/System/CCB Simulator/LSADistilBERT/simulator/FinalModelSimulator.py"
We would like to extend our heartfelt appreciation to Ms. Charisse P. Barbosa, MSIS, our thesis adviser, for the unwavering support and guidance provided throughout the process of completing our thesis, titled "Deep NLP and DistilBERT Conversational Chatbot for Customer Relationship Management.", for her exceptional support, constructive feedback, and guidance. She has been instrumental in shaping the direction of our thesis and enhancing the quality of our work. We are truly fortunate to have had a dedicated and knowledgeable adviser.
We would like to express our gratitude to Mr. Randy F. Ardeña, MCS, Ms. Fe B. Yara, MSIS, and Mr. Meljhon V. Aborde, MIT, the members of our thesis committee, for their valuable insights, thoughtful suggestions, and constructive criticism. Their diverse perspectives have contributed significantly to the depth and rigor of our research. To Mr. Ryan Keath De Leon, an Artificial Intelligence expert for validating our work and for providing recommendations for our Machine Learning model.
We want to acknowledge the College of Computing Education and the University of Mindanao for providing a conducive academic environment for the successful completion of our thesis. The library staff, administrative personnel, and fellow students have all played a part in creating a supportive scholarly community.
We extend our appreciation to each of our family and friends for their encouragement, understanding, and patience throughout this academic journey. Their emotional support has been a source of strength during challenging times.
Once again, we thank you for your support and encouragement. We are truly grateful for the opportunity to have worked with such dedicated individuals and to have been part of this academic community. Your contributions, along with God's blessings, have been instrumental in shaping our academic growth, and We are thankful for the knowledge and skills gained during this process.