TABLE OF CONTENTS

1. Introduction
2. Problem statement
3. Objective
4. Scope of the project
5. System Architecture
6. Tools And Technologies
7. Methodology
a) Data Preprocessing
b) Emotion Detection
c) Response Generation
d) Song Recommendation
e) Integration with UI
8. Proposed System Design
9. Use Cases
10. Expected Outcomes
11. Challenges And Limitation
12. Conclusion
13. Future enhancements
14. References
 INTRODUCTION

In today’s digital world, chatbots are commonly used for

customer service, but they often lack emotional intelligence,
limiting their ability to create meaningful user interactions. This
project, Emotion Detection Chatbot with Personalized Song
Recommendations, aims to bridge that gap by developing a
chatbot capable of detecting user emotions and recommending
songs based on their mood.
Using Natural Language Processing (NLP), the chatbot will
analyze the emotional tone of the conversation (joy, sadness,
anger, etc.) with the help of the IBM Watson Tone Analyzer.
Based on the detected emotion, the chatbot retrieves song
recommendations via the Last.fm API. This fusion of emotion
recognition and personalized music enhances user experience,
making the interaction more human-like and enjoyable.
The project emphasizes AI’s role in everyday applications,
demonstrating how emotional awareness can elevate
interactions, provide comfort, and enhance the user's
emotional well-being through music.
PROBLEM STATEMENT

Reason for Department Approval

The department should approve this project because it explores
cutting-edge AI and NLP technologies, providing practical exposure
to emotion detection and personalized recommendation systems.
It aligns with industry trends in emotional AI and user-centric
applications, bridging technology and mental well-being. By
integrating tools like IBM Watson Tone Analyzer and Last.fm API,
the project offers hands-on learning in real-world AI applications.
It promotes interdisciplinary skills in AI, NLP, and API integration,
preparing students for the evolving tech landscape. Additionally,
the project’s innovative approach highlights AI's potential beyond
business, making it relevant to academia and industry alike.

Strength and Limitation

Strengths of the proposed project include its innovative use of AI
and Natural Language Processing (NLP) for real-time emotion
detection, enhancing chatbot interactions by personalizing user
experiences with music recommendations. It integrates industry-
standard tools like the IBM Watson Tone Analyzer and Last.fm API,
making it practical and highly relevant to emerging trends in
emotional AI and personalized content.
Limitations include the reliance on text-based emotion analysis,
which might miss nuances captured through voice or facial
recognition. Additionally, the system’s accuracy depends on the
quality of available data and may not perform well with complex
or ambiguous emotions.
Positive Impact of Project
The project will have a positive impact on society by promoting
emotional well-being through personalized music
recommendations, offering users a more engaging and empathetic
AI interaction. Academically, it will enhance student knowledge in
AI, NLP, and API integration, providing hands-on experience with
real-world applications. For the college, it highlights innovative
research in emerging technologies, enhancing its reputation in AI
and tech education. In the industry, the project aligns with trends
in emotional AI and personalization, showcasing how AI can
improve user experiences, making it relevant for businesses
seeking to develop smarter, emotion-driven applications.
OBJECTIVE

The primary objective of this project is to develop an Emotion

Detection Chatbot that not only engages users in conversation but
also analyses their emotional state to provide personalized song
recommendations.
The specific objectives include:
1. Emotion Analysis: Utilize the IBM Watson Tone Analyzer API to
accurately detect and analyze the emotional tone of user inputs
in real-time, identifying sentiments such as joy, sadness, and
anger.
2. Song Recommendations: Integrate the Last.fm API to fetch
personalized song recommendations based on the detected
emotions, enhancing user engagement through tailored music
experiences.
3. User Interaction: Create an intuitive and user-friendly interface
that facilitates seamless interactions between users and the
chatbot, making it accessible for diverse audiences.
4. Testing and Refinement: Conduct rigorous testing to ensure the
chatbot’s reliability and effectiveness in emotion detection and
song recommendations, leading to iterative improvements.
5. Educational Value: Provide practical learning experiences for
students in AI, NLP, and API integration, fostering skills relevant
to current industry demands.
SCOPE OF THE PROJECT

The chatbot will be capable of:

• Detecting emotions such as joy, sadness, anger, or neutral tones
from user text.
• Generating appropriate responses based on the detected
emotion.
• Recommending songs from a predefined music database or via
API integration based on emotional analysis.
• Running on a web interface where users can engage in
conversations and receive song recommendations.
This project can be used in various applications:
• Mental health support: Assisting users in mood regulation
through music.
• Entertainment: Providing music recommendations based on
user conversations.
• Education: Helping students or users with interactive learning
experiences enhanced by music.
System Architecture

High-Level Architecture
The chatbot system consists of multiple components working
together:
1. User Interface (UI): A web interface for user interaction.
2. Chatbot Engine: Handles the conversation, processes text input,
and generates appropriate responses.
3. Emotion Detection Module: Processes text input to determine
the emotional tone.
4. Music Recommendation Module: Suggests music based on the
detected emotion.
5. API Integration: Connects the chatbot to external services like
IBM Watson (for emotion detection) and Last.fm/Spotify (for
song recommendations).
Detailed Architecture Diagram
Include a flowchart that shows how the system processes the user
input, detects emotions, generates responses, and recommends
songs.
TOOLS AND TECHNOLOGIES

Programming Language
• Python: Chosen for its rich ecosystem of machine learning, NLP,
and web development libraries.

Libraries
• NLP Libraries: NLTK, SpaCy (for text processing)
• Machine Learning Libraries: TensorFlow, Scikit-learn (for
emotion classification)
• API Libraries: Requests (for Last.fm/Spotify API integration)

Frameworks

• Flask/Django: For building the web interface and RESTful API.

APIs

• IBM Watson Tone Analyzer API: For emotion detection.

• Last.fm API/Spotify API: For music recommendations.
METHODOLOGY

Step 1: Data Preprocessing

• Tokenization, lemmatization, and removing stop words from the
input text.
• Normalizing text for emotion detection algorithms.
•

Step 2: Emotion Detection

• Using IBM Watson Tone Analyzer API, the chatbot will extract
emotions like joy, sadness, anger, or analytical tones from the
text.
•

Step 3: Response Generation

• Once the emotion is detected, a relevant response is generated
based on predefined conversation logic.
•

Step 4: Song Recommendation

• Using the Last.fm or Spotify API, the chatbot will recommend
songs that match the user's detected mood.
• For example, if the emotion is joy, the system will fetch songs
tagged with "happy" or "upbeat."
•

Step 5: Integration with UI

• Build a web interface using Flask/Django, where the user can
interact with the chatbot.
• Display song recommendations in the UI, allowing users to click
and listen to songs.
PROPOSED SYSTEM DESIGN

User Interface (UI) Design

• A clean and user-friendly interface where users can type their
input, view chatbot responses, and get song recommendations.
• Use front-end technologies like HTML, CSS, and JavaScript to
make the interface responsive and interactive.

Backend Design
• A server-side backend built using Flask/Django to handle user
requests, process emotions, and retrieve songs.

Database (Optional)
• Use a database (like SQLite or PostgreSQL) to store
conversation logs, user data, or song recommendations.
USE CASE
In the context of the Emotion Detection Chatbot with Song
Recommendations, the use case can be broken down into 12 key
points, which capture the user’s journey through the system:
1. User Initiation: The user opens the chatbot interface on their
device.
2. Greeting: The chatbot greets the user and prompts them to
start a conversation.
3. User Input: The user types a message or question into the chat.
4. Emotion Detection: The chatbot sends the user’s message to
the IBM Watson Tone Analyzer to identify the emotion behind
the text.
5. Response Generation: Based on the detected emotion, the
chatbot formulates a suitable response.
6. Song Recommendation Request: The chatbot requests song
recommendations from the Last.fm API based on the identified
emotion.
7. Song Retrieval: The Last.fm API returns a list of songs that
match the user's mood.
8. Displaying Response: The chatbot displays its response and the
recommended songs in the chat interface.
9. User Feedback: The user can provide feedback on the song
recommendations.
10. Feedback Processing: The chatbot records the feedback for
future improvements.
11. Continuing Conversation: The user can continue the
conversation by asking more questions or changing the topic.
12. Closing Interaction: The user ends the chat when they feel
satisfied with the interaction.
EXPECTED OUTCOMES

1. Better Conversations: Users will have fun and interesting chats

with the chatbot, making it feel like they are talking to a real
person.
2. Emotion-Based Replies: The chatbot will give responses that
match how the user feels, making the conversations more
meaningful.
3. Personalized Song Suggestions: Users will get music
recommendations based on their mood, making their listening
experience more enjoyable.
4. Quick Emotion Detection: The chatbot will use the IBM Watson
Tone Analyzer to quickly understand how the user feels during
the chat.
5. User Feedback: Users can share their opinions on the song
recommendations, helping to make the chatbot even better
over time.
6. Happier Users: By considering users' feelings and preferences,
the project aims to make users happier with the chatbot
service.
7. Room for New Features: The system will be built in a way that
allows for adding new features in the future, like creating
playlists or connecting with other music apps.
CHALLENGES AND LIMITATIONS

1. Detecting Emotions: It can be hard to accurately figure out how

a user feels based on their text, as people express emotions
differently.
2. Reliance on External Services: The chatbot depends on outside
services like IBM Tone Analyzer and Last.fm, which can
sometimes go offline and affect its performance.
3. Limited User Data: The chatbot may struggle to give good song
recommendations if it doesn't have enough information about
the user's music preferences.
4. Privacy Issues: Collecting and storing user data for emotion
analysis can raise privacy concerns, so it’s important to handle
that information carefully.
5. Technical Difficulties: Connecting different services together can
be complicated, and there may be bugs or errors in the system.
6. Keeping Users Engaged: It can be challenging to maintain users'
interest over time, especially if they don't find the music
recommendations appealing.
7. Varied User Inputs: Users might type in different ways or use
slang, which can confuse the chatbot and make it harder to
respond correctly.
8. Cost of Resources: Running and maintaining the chatbot may
require significant computing power and resources, which can
be expensive.
FUTURE ENHANCEMENTS

1. Voice Interaction: Adding voice recognition would allow users

to talk to the chatbot instead of typing, making it more user-
friendly.
2. Multi-Language Support: Expanding the chatbot to
understand and respond in multiple languages would make it
accessible to more users around the world.
3. Playlist Creation: Introducing a feature that allows users to
create and save personalized playlists based on their favorite
songs.
4. Music History Tracking: Keeping a history of the songs a user
has listened to could help the chatbot make better
recommendations over time.
5. Integration with More Music Platforms: Connecting with
other music services like Spotify or Apple Music to give users
more options for listening.
6. Mood-Based Playlists: Creating playlists based on specific
moods, such as "chill," "happy," or "motivational," to enhance
the user experience.
7. Gamification Features: Adding fun elements like challenges
or rewards for users who interact frequently with the chatbot
could increase engagement.
8. User Customization Options: Allowing users to customize
the chatbot’s personality or theme would make the
experience more personalized and enjoyable.
CONCLUSION

In conclusion, the chatbot project aims to create an engaging

and interactive experience for users by combining casual
conversations with music recommendations. By analyzing the
emotions of users through the IBM Tone Analyzer and
providing personalized song suggestions using the Last.fm API,
this chatbot offers a unique way to enjoy music. While there
are challenges, such as accurately detecting emotions and
maintaining user engagement, the potential benefits are
significant. Future enhancements can further improve the
chatbot's capabilities, making it more accessible and enjoyable
for a wider audience. Overall, this project not only enhances
user experience in music discovery but also provides valuable
learning opportunities in developing advanced chatbots. By
addressing user emotions and preferences, we hope to create
a fun and supportive digital companion that enriches people's
lives through music.
REFERENCES

1. IBM Watson Tone Analyzer: A tool for analyzing emotions

in text.
2. Last.fm API: Provides data on songs and music
recommendations.
3. CakeChat Repository: The code for setting up the CakeChat
chatbot.
4. Chatbot Libraries: Useful Python libraries for chatbot
development.
5. Chatbots Overview: An article explaining chatbot
functionality.
6. Emotion Detection: Research on detecting emotions in text.
7. Flask API Guide: A resource for creating APIs with Flask.
8. API Best Practices: Effective strategies for API integration.