Motivation

This project focuses on text analysis of the Breaking Bad TV show transcripts. Users can explore character dialogues, word frequencies, and sentiment trends to gain insights into the show's narrative and character development.

Breaking Bad is a critically acclaimed American television drama that aired on AMC from 2008 to 2013. The show spans 5 seasons and 62 episodes, and is widely recognized as one of the greatest TV series of all time.

Set in Albuquerque, New Mexico, the series chronicles the transformation of Walter White, a struggling high school chemistry teacher diagnosed with terminal cancer, into the ruthless drug kingpin known as "Heisenberg." Alongside him is his former student and partner, Jesse Pinkman, who navigates the chaos of the methamphetamine trade. Other key characters include Skyler White (Walter's wife), Hank Schrader (Walter's brother-in-law and DEA agent), Saul Goodman (a criminal lawyer), and Gustavo "Gus" Fring (a drug distributor posing as a legitimate businessman).

Breaking Bad blends elements of crime, thriller, and drama, exploring themes of morality, identity, and consequence. The show's rich storytelling and complex character development make it an excellent subject for analysis. This project uses episode transcripts to explore character importance and word usage throughout the series. Visualizations and data-driven insights help highlight each character's role and evolution across the seasons.

Data Source

We pulled transcripts from the open web using an AI-generated Python scraper tailored to the website: https://transcripts.foreverdreaming.org/viewforum.php?f=165

Python Scraper Code

                            
                        import requests
                        from bs4 import BeautifulSoup
                        import os
                        import time
                        
                        # Base URLs
                        base_url = "https://transcripts.foreverdreaming.org"
                        forum_url = f"{base_url}/viewforum.php?f=165"
                        
                        headers = {
                            "User-Agent": "Mozilla/5.0"
                        }
                        
                        # Create output folder
                        os.makedirs("breaking_bad_transcripts", exist_ok=True)
                        
                        # Step 1: Get all thread links from the forum
                        response = requests.get(forum_url, headers=headers)
                        soup = BeautifulSoup(response.text, "html.parser")
                        
                        threads = soup.select("a.topictitle")
                        
                        print(f"Found {len(threads)} threads.")
                        
                        for thread in threads:
                            try:
                                title_raw = thread.text.strip()
                                title = title_raw.replace(" ", "_").replace(":", "").replace("?", "").replace("/", "-")
                                href = thread['href'].split("&")[0]  # remove session ID if present
                                thread_url = base_url + href
                        
                                print(f"Fetching: {title_raw}")
                                thread_response = requests.get(thread_url, headers=headers)
                                thread_soup = BeautifulSoup(thread_response.text, "html.parser")
                                
                                # Step 2: Extract the first post's transcript
                                post_div = thread_soup.find("div", class_="postbody")
                                if post_div:
                                    # Remove quote blocks and signatures
                                    for tag in post_div.select(".quote, .signature"):
                                        tag.decompose()
                        
                                    # Extract clean text
                                    transcript = post_div.get_text(separator="\n", strip=True)
                        
                                    # Save to file
                                    filename = f"breaking_bad_transcripts/{title}.txt"
                                    with open(filename, "w", encoding="utf-8") as f:
                                        f.write(transcript)
                        
                                    print(f"✅ Saved: {filename}")
                                else:
                                    print(f"❌ Transcript not found for {title_raw}")
                        
                                time.sleep(1)  # Be nice to the server
                            except Exception as e:
                                print(f"Error processing {title_raw}: {e}")
                                continue

Visualization Components

  • Line Chart: Displays trends in data over time.
    Line Chart Example
  • Bar Chart: Represents categorical data with rectangular bars.
    Bar Chart Example
  • Pie Chart: Visualizes proportions of categories as slices of a circle.
    Pie Chart Example
  • Arc Cloud: Highlights relationships or connections using arcs.
    Arc Cloud Example
  • Word Cloud: Depicts word frequency with varying font sizes.
    Word Cloud Example

Users can interact with filters to dynamically update the visualizations.

Filters Example

Design Sketches and Justifications

Initial design sketches outline the layout and interactions of the application:

Design Sketch 1 Design Sketch 2 Design Sketch 3 Design Sketch 4 Design Sketch 5 Design Sketch 6

The design prioritizes clarity and usability, ensuring users can easily filter and explore earthquake data.

Development Process

  • Technology Stack: D3.js for data visualization, HTML, CSS, JavaScript for front-end development. Python and Artificial Intelligence utilized for the scraping of the transcripts.
  • Code Repository: GitHub Repository
  • Live Demo: View Application
  • Demo Recording: View Demo Video

GitHub Commit History