Deep Learning Based Natural Language Processing E-Commerce Chatbot


Deep Learning Based Natural Language Processing E-Commerce Chatbot


1. Rudri Jani, Ahmedabad University, Developer, India
2. Nikit Patel, Cygnet Infotech, Developer, India


Nowadays, chatbots are becoming popular because it feels like talking with

humans in natural language in live chat. This research aims to develop an

eCommerce chatbot to help customers buy different pet products from online

pet stores. The system uses deep learning and natural language processing

concepts like text classification, intent classification, multiclass classification,

named entity recognition, etc. The system will guide the customer through

purchasing the products by asking questions and replying. The system will also

extract the entities from the user queries through NER and display the products

accordingly to the user’s request. It will interestingly engage the user in buying

pet products by solving the user’s query and providing a quick response to the

user. This will also increase sales, raising the revenue of the company.


chatbot deep learning natural language processing named en- tity recognition eCommerce text classification multiclass classification rule- based chatbot ElasticSearch MongoDB


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Creating the hybrid model for the chatbot using rule-based and AI-based ap-

proaches rather than only making it rule-based or AI-based has its own ad-

vantages. Using a self-learning approach, a chatbot can reply to the user by

identifying the intent of the trained model. Using a rule-based approach, the

task is performed accordingly if certain conditions are matched. Therefore com-

bining two models and using a hybrid system to develop the chatbot has an

advantage. Using the NER model helps to filter out the entities and search the

products from the database. Introducing the elasticsearch helps to improve the

search efficiency instead of just using regular expressions to find the products

from the database.


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Author Contribution

Rudri Jani has done all research and has participated in the conception and execution of study. Nikit Patel has managed and guided her in the research.


No funding was provided to the author(s) of this article during its research, writing, or publishing.

Software Information

Conflict of Interest

Each author confirms that they have no competing interests.


My mentor Nikit Patel, has been a constant source of inspiration, encouragement, advice, excitement, and belief in my abilities. His knowledge in the IT business, ideas, and suggestions on introducing new things were quite beneficial to me. I’d want to thank him for devoting some of his valuable time to interns like us.

Data availability

Data was created by Rudri Jani relevant to the models trained for the implementation. But the data is confidential so it can't be shared.