Program: Build a Chatbot Using NLP

Below is a Python program that uses Natural Language Processing (NLP) techniques to create a simple conversational chatbot. The chatbot can understand user input and respond appropriately.

import re
import random
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

# Download necessary NLTK resources
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')

# Predefined responses for the chatbot
responses = {
    "greeting": ["Hello! How can I help you?", "Hi there! What can I do for you?", "Hey! What's on your mind?"],
    "farewell": ["Goodbye! Have a great day!", "See you later!", "Bye! Take care!"],
    "thanks": ["You're welcome!", "No problem!", "Happy to help!"],
    "default": ["I'm sorry, I didn't understand that.", "Could you please rephrase that?", "I'm not sure what you mean."]
}

def preprocess_input(user_input):
    """
    Preprocess the user input by cleaning, tokenizing, and lemmatizing it.
    """
    # Convert text to lowercase
    user_input = user_input.lower()

    # Remove special characters and numbers
    user_input = re.sub(r'[^a-zA-Z\s]', '', user_input)

    # Tokenize words
    words = word_tokenize(user_input)

    # Remove stopwords
    stop_words = set(stopwords.words('english'))
    filtered_words = [word for word in words if word not in stop_words]

    # Lemmatize words
    lemmatizer = WordNetLemmatizer()
    lemmatized_words = [lemmatizer.lemmatize(word) for word in filtered_words]

    return lemmatized_words

def get_response(intent):
    """
    Get a random response based on the detected intent.
    """
    return random.choice(responses.get(intent, responses["default"]))

def detect_intent(user_input):
    """
    Detect the intent of the user input based on keywords.
    """
    greeting_keywords = ["hello", "hi", "hey"]
    farewell_keywords = ["bye", "goodbye", "see you"]
    thanks_keywords = ["thanks", "thank you", "appreciate"]

    if any(word in user_input for word in greeting_keywords):
        return "greeting"
    elif any(word in user_input for word in farewell_keywords):
        return "farewell"
    elif any(word in user_input for word in thanks_keywords):
        return "thanks"
    else:
        return "default"

def chatbot():
    """
    Run the chatbot and interact with the user.
    """
    print("Chatbot: Hello! I'm your friendly chatbot. Type 'exit' to end the conversation.")
    while True:
        user_input = input("You: ")
        if user_input.lower() == "exit":
            print("Chatbot: Goodbye!")
            break

        # Preprocess user input
        processed_input = preprocess_input(user_input)

        # Detect intent
        intent = detect_intent(processed_input)

        # Get and print response
        response = get_response(intent)
        print(f"Chatbot: {response}")

# Run the chatbot
chatbot()

Explanation of the Program

Preprocessing User Input:
- The preprocess_input function cleans the user input by converting it to lowercase, removing special characters and numbers, tokenizing words, removing stopwords, and lemmatizing words.
Detecting Intent:
- The detect_intent function identifies the user’s intent based on predefined keywords. For example, if the input contains “hello” or “hi,” the intent is classified as a greeting.
Generating Responses:
- The get_response function selects a random response from the predefined responses based on the detected intent.
Chatbot Interaction:
- The chatbot function runs the chatbot, allowing the user to interact with it. The conversation continues until the user types “exit.”

Example Conversation

Chatbot: Hello! I'm your friendly chatbot. Type 'exit' to end the conversation.
You: Hi there!
Chatbot: Hi there! What can I do for you?
You: How are you?
Chatbot: I'm sorry, I didn't understand that.
You: Thank you for your help!
Chatbot: You're welcome!
You: Goodbye
Chatbot: Goodbye! Have a great day!

Benefits of Using NLP for Chatbots

Improved Understanding: NLP techniques like tokenization and lemmatization help the chatbot better understand user input.
Personalized Responses: By detecting intent, the chatbot can provide more relevant and personalized responses.
Scalability: This program can be extended to handle more complex conversations and integrate with APIs for advanced functionality.

This program is a simple yet effective way to build a chatbot using NLP techniques.

Natural Language Processing

Fundamental Concepts

Text Processing & Cleaning

Tools, Libraries & APIs

Program(s)

Program: Build a Chatbot Using NLP

Explanation of the Program

Example Conversation

Benefits of Using NLP for Chatbots