Building a NLP Chatbot in Python

Deepak Ranolia
2 min readNov 19


Introduction: Chatbots powered by Natural Language Processing (NLP) have become integral for providing seamless interactions between users and applications. In this article, we’ll explore the implementation of a Python-based NLP chatbot incorporating key intents for a more engaging and responsive user experience.

Setting Up the Environment: Before diving into the code, ensure you have the necessary libraries installed. Use pip to install the following:

pip install nltk
pip install spacy
pip install python-dotenv
pip install transformers

Building the Chatbot:

Importing Libraries:

import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
import spacy
import transformers

Defining Intent Functions:

def greeting_intent(user_message):
# Implement greeting logic
return "Welcome! How can I assist you today?"

def user_query_intent(user_message):
# Implement user query logic
return "I understand your query. Here is the relevant information."

def clarification_intent(user_message):
# Implement clarification logic
return "Certainly! Could you please provide more details?"

def action_request_intent(user_message):
# Implement action request logic
return "Sure, let me handle that for you."

def fallback_intent(user_message):
# Implement fallback logic
return "I'm sorry, I didn't quite catch that. Could you please rephrase?"

def closing_intent(user_message):
# Implement closing logic
return "Thank you for chatting with me. Have a great day!"

def feedback_intent(user_message):
# Implement feedback logic
return "Thank you for your feedback. We appreciate it!"

Intent Recognition:

def recognize_intent(user_message):
# Tokenizing user message
tokens = word_tokenize(user_message.lower())

# Checking for intent keywords
if any(word in tokens for word in ["hello", "hi", "hey"]):
return greeting_intent(user_message)
elif any(word in tokens for word in ["query", "request"]):
return user_query_intent(user_message)
elif any(word in tokens for word in ["clarify", "details"]):
return clarification_intent(user_message)
elif any(word in tokens for word in ["action", "task"]):
return action_request_intent(user_message)
elif any(word in tokens for word in ["fallback", "unknown"]):
return fallback_intent(user_message)
elif any(word in tokens for word in ["bye", "thank you"]):
return closing_intent(user_message)
elif any(word in tokens for word in ["feedback", "improvement"]):
return feedback_intent(user_message)
return fallback_intent(user_message)

Conclusion: This Python-based NLP chatbot serves as a foundation for creating a more interactive and user-friendly conversational agent. Feel free to enhance and customize the intents based on your specific use case, making the chatbot a powerful tool for engaging with users effectively.



Deepak Ranolia

Strong technical skills, such as coding, software engineering, data analysis, and project management. Talk about finance, technology & life