A Report on

Video Chaptering & Analysis

Submitted for partial fulfillment of award of

BACHELOR OF TECHNOLOGY

In

Computer Science & Engineering

(Artificial Intelligence & Machine Learning)

By

Aryaman Nair (2100301530018)

Anand Singh (2100301530009)
Divyansh Aggarwal (2100301530033)

INDERPRASTHA ENGINEERING COLLEGE, GHAZIABAD,

Dr. A P J ABDUL KALAM TECHNICAL UNIVERSITY

LUCKNOW
December 2024

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 Certificate

Certified that Aryaman Nair, Anand Singh, Divyansh Aggarwal have

carried out the project work presented in this report entitled “Video Chaptering &
Analysis” for the award of Bachelor of Technology from Inderprastha Engineering
College, Ghaziabad, under my supervision. The report embodies result of original
work and studies carried out by Student himself/herself and the contents of the report
do not form the basis for the award of any other degree to the candidate or to anybody
else.

Submitted to: Dr. Ruchika Bala

Date: 03/12/2024

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 Acknowledgement

We take this opportunity to thank our teachers and friends who helped us throughout the
project.

We would like to express my sincere gratitude to everyone who has contributed to the
successful completion of this report.

First and foremost, I extend my deepest thanks Dr. Kumud Kundu (HOD, Mrs. Neha
Verma, Mrs. Ruchika Bala, Mrs. Uma Sharma, Computer Science and Engineering
(Artificial Intelligence & Machine Learning), whose guidance, insights, and expertise were
invaluable throughout the process. Their constant support and encouragement have been
instrumental in shaping this work.

I am also grateful to Indraprastha Engineering College for providing the necessary resources
and a conducive environment for carrying out this project.

Special thanks to my peers and colleagues, Mr. Aman Ojha, Mr. Gaurav Sharma, Mr.
Dhruv for their valuable feedback and suggestions that greatly improved the quality of this
work.

Lastly, I would like to thank my family and friends for their unwavering support and
motivation during this endeavour.

This report is the culmination of collective efforts, and we are truly appreciative of everyone
who has played a role in its creation.

Aryaman Nair Anand Singh Divyansh

Aggarwal

Roll No.: 2100301530018 Roll No.: 2100301530009 Roll

No.: 2100301530033

____________________ _____________________
_____________________
Signature Signature
Signature

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 Abstract

The Video Chaptering & Analysis project aims to enhance the accessibility and usability of
video content by automatically segmenting videos into meaningful chapters and performing
sentiment analysis. Leveraging advanced Natural Language Processing (NLP) and Machine
Learning (ML) techniques, the system transcribes audio content from videos and analyzes the
resulting text for key topics, themes, and logical transitions. This enables structured
navigation, allowing users to locate specific sections of interest quickly.

This innovative approach has applications in e-learning, content creation, and marketing by
enhancing video navigation and deriving actionable insights. The modular architecture
ensures scalability, and the use of widely supported tools and APIs makes the system robust
and adaptable for real-world scenarios.

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 Table of Contents

S.No. Title Page No.

1 Certificate 02

2 Acknowledgement 03

3 Abstract 04

4 Introduction 06

5 Software Requirement Analysis 07

6 FEASIBILITY STUDY 08-09

7 SYSTEM ANALYSIS & 10-11

DESIGN
8 IMPLEMENTATION/CORE MOD- 12
ULE

9 RESULTS / OUTPUTS & TESTING 13

10 CONCLUSIONS / RECOMMENDA- 14
TIOS

11 REFERENCES 15

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Chapter 1
Introduction
The rapid growth of video content on platforms like YouTube and educational repositories
has created an increasing demand for tools to enhance video accessibility and navigation.
Video Chaptering and Analysis is a technological innovation aimed at segmenting videos into
coherent and meaningful chapters. This process leverages advanced Natural Language
Processing (NLP) and Machine Learning (ML) techniques to analyse the video’s audio
transcript, identify key topics, and determine logical transitions between segments. By
structuring video content into chapters, users can easily navigate to specific sections of
interest, improving their overall viewing experience.

The process of video chaptering begins with audio extraction from the video, followed by
transcription into text. The transcribed text is then analysed using NLP methods to detect
themes, topics, and transitional points within the content. In addition to chapter segmentation,
sentiment analysis is performed to understand the emotional tone of the video. This dual
functionality not only provides structured navigation but also enables insights into the video’s
impact and emotional undertones, making it a valuable tool for content creators, educators,
and businesses.

To implement Video Chaptering and Analysis, data collection is a critical first step. For this
project, audio data from videos is obtained using the YouTube Data API. By enabling access
to metadata and media files, the API provides a seamless way to collect relevant data for
transcription and analysis. This innovative approach has broad applications, ranging from
improving e-learning platforms to enhancing marketing strategies by tailoring content based
on emotional and thematic insights.

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Chapter 2
Software Requirement Analysis
1. Operating System
 Windows: Version 10 or later.
 Linux: Ubuntu 20.04 or higher.
 macOS: Version Monterey or later.

2. Programming Language
 Python (v3.12.5

3. APIs and External Tools

A. YouTube Data API v3
 Purpose: Retrieve video data (e.g., audio, metadata).
B. youtube-transcript-api
 Purpose: Extract subtitles or captions (if available) directly from YouTube videos to
enhance transcription accuracy.

4. Python Libraries and Frameworks

Core Libraries
1. Pandas: Data manipulation and structuring.
2. NumPy: Numerical operations and array manipulations.
Video and Audio Processing
1. you tube-transcript-api: Extract transcripts directly from YouTube videos.
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 2. moviepy: Handle video and audio processing.
Natural Language Processing (NLP)
1. NLTK: Tokenization, text processing, and linguistic analysis
2. Scikit-learn (v1.5.1): Implement machine learning algorithms like clustering for topic
detection.
Visualization Tools
1. Matplotlib: Data visualization for sentiment trends and chaptering results
o Streamlit: Develop an interactive user interface for the application.
Deployment Tools
1. Streamlit: for deploying our application on the web.

Chapter 3
Feasibility Study
The feasibility study evaluates the technical, operational, and scheduling aspects of the Video
Chaptering & Analysis project. This ensures that the project is viable, achievable, and
beneficial in its intended context.

1. Technical Feasibility

A. Technology Requirements
1. Software Tools:
o Python libraries (e.g., pytube, moviepy, Speech Recognition, spaCy,
TextBlob).
o YouTube Data API for video data collection.
o Visualization tools like Matplotlib for data representation.
2. Hardware Requirements:
o A computer with a quad-core processor, 8 GB of RAM (minimum),
and 10 GB free disk space.
o Optional GPU support for faster execution of ML-based tasks.
3. System Dependencies:
o FFmpeg for audio and video processing.
o Speech-to-text tools for transcription, such as Google Speech API.
B. Complexity
 Moderate: The system requires a mix of video processing, transcription, NLP, and
sentiment analysis, all of which are achievable using widely supported Python
libraries and frameworks.
C. Scalability

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  The project can scale to handle larger datasets or integrate real-time processing by
upgrading hardware and optimizing code for parallel execution.

2. Operational Feasibility

A. Skill Requirements
1. Developers with experience in:
o Python programming.
o Video and audio processing.
o NLP and Machine Learning techniques.
2. Understanding of YouTube Data API usage and configuration.
B. End-User Impact
1. Ease of Use: The tool should have an intuitive interface, allowing users to input video
URLs and receive structured chapters and sentiment insights.
2. Applications:
o Educators can use it to create navigable course videos.
o Content creators can analyse video performance and improve viewer
engagement.

3. Scheduling Feasibility

A. Development Timeline
A project timeline of 12–16 weeks is feasible for the following tasks:
1. Week 1–2: Requirement gathering and environment setup.
2. Week 3–5: Data collection and audio transcription integration.
3. Week 6–8: Implementation of chaptering logic using NLP techniques.
4. Week 9–10: Sentiment analysis module integration.
5. Week 11–12: Testing and debugging.
6. Week 13–14: Visualization and user interface creation.
7. Week 15–16: Documentation and final report preparation.
B. Dependencies
 Availability of APIs, especially YouTube Data API, with proper quota allocation.
 Access to high-quality video and audio datasets for testing and evaluation.

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Chapter 4
System Analysis & Design
The Video Chaptering & Analysis project involves designing a robust system capable of
segmenting videos into logical chapters and analysing their emotional tone. This section
outlines the system's functional and non-functional requirements, the proposed system
architecture, and the design methodology.

1. System Analysis

A. Functional Requirements
1. Video Data Collection:

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 o Ability to download video and audio data from YouTube using the YouTube
Data API.
o Extract metadata such as title, description, and duration.
2. Audio Processing:
o Extract audio from video files.
o Preprocess audio to enhance quality for transcription.
3. Transcription:
o Convert audio into text using a speech-to-text engine.
o Ensure accurate transcription of spoken content.
4. Natural Language Processing (NLP):
o Analyse the text to identify key topics and logical transitions.
o Divide the content into coherent chapters.
5. Sentiment Analysis:
o Perform sentiment analysis on the text to capture emotional tone.
o Provide insights such as positive, negative, or neutral sentiment trends.
6. Visualization:
o Display chapter segmentation and sentiment trends visually for better
understanding.
7. User Interface:
o Provide an interface for users to input YouTube video URLs and view results.
B. Non-Functional Requirements
1. Performance:
o Process standard-length videos (10–60 minutes) within a reasonable time
frame.
2. Scalability:
o Ability to handle longer videos or batch processing with minimal performance
degradation.
3. Accuracy:
o High transcription accuracy for clear audio & Reliable sentiment classification
and chaptering results.
4. Usability:
o Easy-to-use interface for non-technical users.

2. System Design

A. System Architecture
1. Input Layer:
2. Preprocessing Layer:
3. Processing Layer:
4. Output Layer:
5. Backend Layer:
6. Frontend Layer:
B. Algorithms
1. Chapter Identification Algorithm
2. Sentiment Analysis Algorithm
C. Technology Stack
1. Programming Language: Python
2. Libraries:
o Video Processing: pytube, moviepy

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 o NLP: spaCy, NLTK
o Sentiment Analysis: TextBlob, Scikit-learn
3. APIs: YouTube Data API
4. Visualization Tools: Matplotlib, Seaborn
5. Backend: Flask or Fast API

3. User Interface Design

A. Input Section:
 Text box to enter the YouTube video URL.
 Button to upload local video files (optional).
B. Output Section:
 List of chapters with titles and timestamps.
 Sentiment trends displayed as a line graph or bar chart.

Chapter 5

Implementation / Core Module

The implementation of the Video Chaptering & Analysis project involves integrating several
components, including video processing, transcription, NLP, and sentiment analysis. This
section details the core modules, their implementation steps, and the technologies used.

1. Core Modules Overview

The system is divided into the following key modules:

1. Video Data Collection Module
2. Audio Processing Module

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 3. Transcription Module
4. Chapter Segmentation Module
5. Sentiment Analysis Module
6. Visualization Module

2. Module Descriptions and Implementation

A. Video Data Collection Module

Purpose: To collect video and metadata from YouTube.

B. Audio Processing Module

Purpose: To extract and preprocess audio from the video.

C. Transcription Module
Purpose: To convert audio content into text using speech-to-text techniques.

D. Chapter Segmentation Module

Purpose: To divide transcribed text into meaningful chapters.

E. Sentiment Analysis Module

Purpose: To analyse the emotional tone of each chapter.

F. Visualization Module
Purpose: To display chapters and sentiment trends.

Chapter 6
Results / Outputs
This section presents the results obtained from the implemented system and the testing
process to validate its functionality, performance, and reliability.

1. Results / Outputs

The outputs from the Video Chaptering & Analysis system include:

A. Chapter Segmentation Results

1. Input: A YouTube video URL.

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 2. Output: A list of chapters with titles and timestamps.
o Example Output:
o Chapter 1: Introduction (0:00–2:15)
o Chapter 2: Key Concepts (2:16–5:40)
o Chapter 3: Examples and Use Cases (5:41–9:10)
o Chapter 4: Conclusion (9:11–10:30)

B. Sentiment Analysis Results

1. Input: Text data from the transcribed audio.
2. Output: Sentiment classification for each chapter (Positive, Negative, Neutral).
o Example Output:
o Chapter 1: Neutral
o Chapter 2: Positive
o Chapter 3: Positive
o Chapter 4: Neutral

C. User Interface Output

1. Input: User provides the YouTube video URL or uploads a video file.
2. Output: Processed results displayed in a clean interface.

(Figure 1. It describe how much large the words are and how much is their frequency)

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 (Figure 2. It the number of occurrence of words)

(Figure 3. It describe the sentimental analysis of the video)

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Chapter 7
Conclusions / Recommendations
Conclusions
The Video Chaptering & Analysis project demonstrates the ability to automate the
segmentation and analysis of video content using advanced techniques in Natural
Language Processing (NLP) and Machine Learning (ML).
Key outcomes of the project include:
o Efficient Chaptering: The system successfully segments videos into coherent
chapters based on audio content, enabling users to navigate specific sections
quickly.
o Sentiment Insights: By analysing emotional tones, the system provides
valuable sentiment trends across different chapters, benefiting content
creators and educators.
o Automation and Scalability: The modular design ensures the system can
handle various video lengths and content types with minimal manual
intervention.
The results highlight the potential of integrating video processing, NLP, and
sentiment analysis to improve user experience, especially in domains like e-learning,
media content management, and digital marketing.

Recommendations
Based on the project outcomes, the following recommendations are proposed for
future enhancements and applications:
1. Technical Improvements
 Multi-Language Support
 Improved Accuracy
2. Additional Features
 Real-Time Processing
 Customizable Chapter Titles
 Advanced Analytics
3. User Experience Enhancements
 Interactive Interface
 Export Options
 4. Deployment Recommendations
 Cloud Deployment.
 API Integration.
5. Applications in Diverse Domains
 Education
 Media and Marketing
 Healthcare

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Chapter 8

References
1. Python Software Foundation, "Python 3.12.5 Documentation."
https://www.python.org/doc/.
2. Pandas Development Team, "Pandas v2.2.2 Documentation."
https://pandas.pydata.org/docs/index.html.
3. NumPy Developers, "NumPy v2.1 Documentation."
https://numpy.org/doc/stable/.
4. Google Developers, "Google API Python Client."
https://github.com/googleapis/google-api-python-client.
5. Python Package Index, "youtube-transcript-api v0.6.2 Documentation."
https://pypi.org/project/youtube-transcript-api/.
6. Streamlit Inc., "Streamlit v1.37.0 Documentation."
https://docs.streamlit.io/.
7. Scikit-learn Developers, "Scikit-learn v1.5.1 Documentation."
https://scikit-learn.org/0.21/documentation.html.
8. Bird, Steven, Edward Loper, and Ewan Klein. "Natural Language Toolkit (NLTK)
v3.12 Documentation."
https://www.nltk.org/_modules/nltk.html.
9. Hunter, John D., et al., "Matplotlib v3.9.2 Documentation."
https://matplotlib.org/stable/index.html.
10. FFmpeg Developers, "FFmpeg Documentation."
https://ffmpeg.org/.

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