Chatbot using Python, NLP, and Data Science | Build Your Own Chatbot | Intellipaat

Chatbot using Python, NLP, and Data Science | Build Your Own Chatbot | Intellipaat

Script

Hello everyone, welcome to IntelliPath's live webinar on creating a chatbot using Python, NLP, and Data Science. Today's webinar will be conducted by Mr. Ankit Tanmay, who is a Senior Data Scientist with eight years of experience in providing data-driven solutions to complex business problems. Presently, he is working at Microsoft as a Data and Applied Scientist. Let's welcome Mr. Ankit into the session.

Thank you for the warm welcome. Hi, everyone. It's great to meet and interact with AI and ML enthusiasts, as well as experts in this field. Today, we will delve into chatbots and cover key concepts, practical implementation, and coding, all while being mindful of the time and content demands.

Agenda

1. Brief overview of chatbots
2. Need for chatbots
3. Types of chatbots
4. Rule-based chatbots
5. Self-learning chatbots
6. Practical implementation in Python and NLTK

Introduction to Chatbots

A chatbot is a software application used to conduct online chat conversations via text or text-to-speech without contact with a human agent. This technology is a significant example of AI in action, capable of solving complex business problems.

Historical Context

The first chatbot, Eliza, was created in 1966. Eliza recognized keywords or phrases in input to provide a meaningful conversational flow. The efficiency of AI systems like chatbots is measured by the Turing test, helping determine their human-like accuracy.

The Need for Chatbots

Chatbots have numerous applications, including:

1. Messaging Apps: Interacting with customers for services and interactions.
2. Customer Service: They handle queries and issues, reducing the need for human agents.
3. Company Internal Use: Automated responses for HR, finance, and business inquiries within organizations.

Types of Chatbots

1. Simple Chatbots: Task-specific and rule-based.
2. Smart Chatbots: AI-enabled chatbots that simulate human interactions.
3. Hybrid Chatbots: Combines rule-based and AI chatbots.
4. Social Messaging Chatbots: Used on platforms like WhatsApp or Telegram.
5. Menu-Based Chatbots: Offer predefined options to the user.

Our focus will be primarily on rule-based chatbots and self-learning (reinforcement) chatbots.

Rule-Based vs. Self-Learning Chatbots

1. Rule-Based Chatbots
   • Function on predefined rules.
   • Respond based on a formulated set of questions and answers.
2. Self-Learning Chatbots
   • Employ machine learning to provide suitable answers.
   • They continuously grow and improve by learning from past interactions.

Rule-Based Chatbot Implementation

The process involves:

1. Recognizing user intent.
2. Matching user input with predefined commands.
3. Selecting a relevant response.

Here’s an example of creating a rule-based chatbot using Python:

Example Code Snippet

import re
import random

class RuleBot:
    def __init__(self):
        self.negative_responses = ("no", "nope", "nah", "not a chance")
        self.exit_commands = ("quit", "pause", "exit", "goodbye", "bye")
        self.rand_questions = (
            "How are you?", "What's up?", "How are you doing?"
        )
        self.alien_babble = (
            'describe_planet_intent': r'.*\s*your planet.*',
            'answer_why_intent': r'.*\s*why.*',
            'about_intellipaat_intent': r'.*\s*intellipaat.*'
        )
    
    def greet(self):
        self.name = input("What's your name?")
        return f"Hi (self.name), I'm a robot. Will you help me learn about your planet?"
    
    def make_exit(self, reply):
        for exit_command in self.exit_commands:
            if exit_command in reply:
                print("Okay, have a nice Earth day!")
                return True
        return False
    
    def match_reply(self, reply):
        for intent, pattern in self.alien_babble.items():
            if re.match(pattern, reply):
                if intent == 'describe_planet_intent':
                    return random.choice(["My planet is Elara", "I come from a distant galaxy"])
                elif intent == 'answer_why_intent':
                    return random.choice(["I want to learn more about humans", "I seek knowledge"])
                elif intent == 'about_intellipaat_intent':
                    return random.choice(["Intellipaat is a great place to learn AI!", "Intellipaat offers great courses!"])
        return random.choice(["Tell me more!", "Why do you think so?", "I need more information"])

Self-Learning Chatbot Implementation Using NLTK

Let’s now explore building a more intelligent chatbot using Python and NLTK. This chatbot will read a corpus of text, preprocess it, and use cosine similarity for fetching responses.

Example Code Snippet

import nltk
from nltk.stem import WordNetLemmatizer
import numpy as np
import random
import string
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

## Introduction
raw_text = open('<path-to-your-data>.txt', 'r', errors='ignore').read().lower()

## Introduction
sent_tokens = nltk.sent_tokenize(raw_text)
word_tokens = nltk.word_tokenize(raw_text)

lemmatizer = WordNetLemmatizer()

def LemTokens(tokens):
    return [lemmatizer.lemmatize(token) for token in tokens if token not in punctuation_remover]

def LemNormalize(text):
    return LemTokens(nltk.word_tokenize(text.lower().translate(punctuation_remover)))

## Introduction
def response(user_response):
    robo_response = ''
    TfidfVec = TfidfVectorizer(tokenizer=LemNormalize, stop_words='english')
    tfidf = TfidfVec.fit_transform(sent_tokens)
    vals = cosine_similarity(tfidf[-1], tfidf)
    idx = vals.argsort()[0][-2]
    flat = vals.flatten()
    flat.sort()
    req_tfidf = flat[-2]
    if req_tfidf == 0:
        robo_response = "I'm sorry! I don't understand you."
        return robo_response
    else:
        robo_response = robo_response + sent_tokens[idx]
        return robo_response

## Introduction
flag = True
print("ChatBot: I'm your Bot. Type 'bye' to exit")

while flag:
    user_response = input()
    user_response = user_response.lower()
    if user_response != 'bye':
        if user_response == 'thanks' or user_response == 'thank you':
            flag = False
            print("ChatBot: You're welcome!")
        else:
            if greeting(user_response) is not None:
                print("ChatBot: " + greeting(user_response))
            else:
                sent_tokens.append(user_response)
                word_tokens = word_tokens + nltk.word_tokenize(user_response)
                final_words = set(word_tokens)
                print("ChatBot: ", end="")
                print(response(user_response))
                sent_tokens.remove(user_response)
    else:
        flag = False
        print("ChatBot: Goodbye!")

This chatbot uses a text file for learning and responds based on the closest match using cosine similarity.

Conclusion Chatbots are versatile applications of AI and NLP that can significantly improve customer service and automation within organizations. By understanding both rule-based and self-learning chatbots, you can leverage Python and NLTK to build your own intelligent conversational agents.

Keywords

• Chatbot
• Rule-Based Chatbots
• Self-Learning Chatbots
• Python
• NLTK
• Data Science
• Turing Test
• Text Processing
• Tokenization
• Lemmatization
• Cosine Similarity
• TF-IDF Vectorizer

FAQ

What is a chatbot?

A chatbot is a software application designed to simulate conversation with human users, especially over the Internet.

What are the types of chatbots?

Chatbots can be classified into simple chatbots, smart chatbots, hybrid chatbots, social messaging chatbots, and menu-based chatbots.

What is the difference between a rule-based and a self-learning chatbot?

• Rule-based chatbots use predefined rules and responses.
• Self-learning chatbots utilize machine learning algorithms to learn and provide suitable answers.

What libraries are used to implement a chatbot in Python?

Commonly used libraries include nltk, re, random, numpy, sklearn for different functionalities in the chatbot pipeline.

What is tokenization in NLP?

Tokenization is the process of splitting text into individual words or phrases, which are called tokens.

How does cosine similarity work in chatbot development?

Cosine similarity measures the similarity between two vectors of an inner product space, which helps in identifying the most relevant sentence from a text corpus in response to the user's query.

What is TF-IDF Vectorizer?

TF-IDF (Term Frequency-Inverse Document Frequency) Vectorizer converts a collection of raw documents into a matrix of TF-IDF features, giving more importance to rare words in the document.