Chatbot Development Using LSTM and TensorFlow

Abstract

This document presents a comprehensive
implementation of a chatbot system that automates
customer support queries, built using deep learning
techniques. The chatbot is designed to interact with
users and provide relevant responses to a wide range of
frequently asked questions (FAQs) regarding an ecommerce
platform. The chatbot model is built using
TensorFlow and Keras, leveraging Natural Language
Processing (NLP) techniques such as tokenization,
stemming, and sequence padding to preprocess textual
data.The core of the chatbot’s model consists of a Long
Short-Term Memory (LSTM) network, which is trained
on a pre-cleaned dataset of customer queries. This
neural network learns to classify queries into specific
categories, such as product returns, payments, delivery,
and refunds. The model is further enhanced with an
interactive mechanism, where the bot learns from user
feedback and updates its knowledge base by
incorporating new queries and responses into the
dataset. Additionally, the chatbot provides the
functionality to handle follow-up queries from users,
offering context-aware responses based on previous
interactions. The system is designed to continuously
improve its accuracy by updating the model after every
25 successful interactions, making it adaptable to new
and unseen queries. To ensure robustness, the chatbot
offers fallback mechanisms for cases where the system
is unsure of the correct response. Users are encouraged
to provide feedback on the bot’s responses, and the
system adapts by either improving its responses or
requesting more specific information to resolve the
query. In conclusion, this chatbot serves as a scalable
and responsive solution for automating customer
support, enhancing user experience by providing
instant and accurate responses, and continuously
improving through feedback loops. This approach
offers a practical implementation of deep learning for
real-time customer service automation in the ecommerce
industry.