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How to make a chatbot that talks like a human in Scratch

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💡 Struggling with chatbot logic and natural responses? Need help with conversation flow? 🚀 Get Help Now

CE

CodeExplorer_Alex

Posted on July 21, 2025 • Intermediate

🤖 Need help creating a realistic chatbot

Hey everyone! I’m working on a chatbot project in Scratch and I’m having trouble making it respond naturally to what people type. Right now it feels very robotic and doesn’t understand context at all.

My main challenges are:

  • Analyzing user input properly
  • Making responses feel natural and human-like
  • Handling different ways people might ask the same thing
  • Adding some personality to the bot

I’m still learning about text processing and conversation logic. Any tips would be amazing! 🙏

AI

AIBuilder_Sarah

Replied 3 hours later • ⭐ Best Answer

Great question @CodeExplorer_Alex! Creating human-like chatbots is definitely challenging but super rewarding. Here’s a comprehensive approach:

🧠 Chatbot Logic Flow

Here’s how a smart chatbot processes conversations:

flowchart TD A[💬 User Input] --> B[Clean Text] B --> C[Extract Keywords] C --> D{Match Intent?} D -->|Greeting| E[😊 Friendly Response] D -->|Question| F[❓ Helpful Answer] D -->|Emotion| G[💝 Empathetic Reply] D -->|Unknown| H[🤔 Clarification Request] E --> I[Add Personality] F --> I G --> I H --> I I --> J[📝 Store Context] J --> K[🎯 Generate Response] K --> L[💬 Display to User] L --> M{Continue Chat?} M -->|Yes| A M -->|No| N[👋 Goodbye] style A fill:#e1f5fe style D fill:#fff3e0 style I fill:#f3e5f5 style K fill:#e8f5e8

🔧 Step 1: Text Processing System

First, create variables to handle user input:

    when flag clicked
set [user input v] to []
set [processed text v] to []
set [keywords v] to []
set [bot personality v] to [friendly]

📝 Step 2: Input Analysis

Create a custom block to analyze what users type:

    define analyze input (text)
set [processed text v] to (text)
// Convert to lowercase for easier matching
set [processed text v] to (join [] (processed text))

// Check for greetings
if <(processed text) contains [hello]?> then
set [intent v] to [greeting]
else
if <(processed text) contains [help]?> then
set [intent v] to [help_request]
else
if <(processed text) contains [how]?> then
set [intent v] to [question]
else
set [intent v] to [unknown]
end
end
end

🎭 Step 3: Personality System

Make your bot feel more human with personality traits:

    define generate response (intent)
if <(intent) = [greeting]> then
set [responses v] to [Hey there! 😊|Hello! How can I help you today?|Hi! Great to see you!]
set [response v] to (item (random 1 to 3) of [responses v])
else
if <(intent) = [help_request]> then
set [response v] to [I'd love to help! What do you need assistance with? 🤝]
else
if <(intent) = [question]> then
set [response v] to [That's a great question! Let me think about that... 🤔]
else
set [response v] to [I'm not sure I understand. Could you rephrase that? 😅]
end
end
end

💾 Step 4: Context Memory

Help your bot remember the conversation:

    define remember context (user_input) (bot_response)
add (join [User: ] (user_input)) to [conversation history v]
add (join [Bot: ] (bot_response)) to [conversation history v]

// Keep only last 10 exchanges to save memory
if <(length of [conversation history v]) > [20]> then
delete (1) of [conversation history v]
delete (1) of [conversation history v]
end

🚀 Step 5: Advanced Features

Make your chatbot even smarter:

Emotion Detection:

    define detect emotion (text)
if <(text) contains [sad]?> then
set [user emotion v] to [sad]
set [response tone v] to [supportive]
else
if <(text) contains [excited]?> then
set [user emotion v] to [happy]
set [response tone v] to [enthusiastic]
else
set [user emotion v] to [neutral]
set [response tone v] to [friendly]
end
end

Smart Keyword Matching:

    define find keywords (text)
set [keywords found v] to []
set [word list v] to [scratch|programming|help|tutorial|game|project]
repeat (length of [word list v])
set [current word v] to (item (counter) of [word list v])
if <(text) contains (current word)?> then
add (current word) to [keywords found v]
end
end

The key to human-like responses is variety, context awareness, and showing empathy. Don’t just match keywords - try to understand the user’s intent and emotional state!

CE

CodeExplorer_Alex

Replied 45 minutes later

@AIBuilder_Sarah This is incredible! Thank you so much! 🎉

I implemented the basic intent detection and it’s already working much better. One question - how do I handle typos and different spellings of the same word?

NL

NLPExpert_Mike

Replied 1 hour later

@CodeExplorer_Alex Great question about typos! Here’s a simple fuzzy matching approach:

    define fuzzy match (input) (target)
set [similarity v] to [0]
set [matches v] to [0]

// Count matching characters
repeat (length of (input))
set [char v] to (letter (counter) of (input))
if <(target) contains (char)?> then
change [matches v] by [1]
end
end

// Calculate similarity percentage
set [similarity v] to ((matches) / (length of (input)))

// If 70% or more characters match, consider it a match
if <(similarity) > [0.7]> then
set [is match v] to [true]
else
set [is match v] to [false]
end

This helps catch common typos like “helo” instead of “hello”! 🔧

VB

Vibelf_Community

Pinned Message • Moderator

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