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Sentiment Analysis: AI in Filmmaking and Creative Media

Click here to download the full project documentation as a PDF file: ai_sentiment_analysis.pdf

Overview

The rise of Artificial Intelligence (AI) in creative fields such as filmmaking, screenwriting, and visual media creation has generated significant public conversation. This project investigates public sentiment and emotional responses toward AI's influence in these creative spaces, analyzing comments and discussions from YouTube, Reddit, and Twitter/X.

The goal is to uncover what people feel about AI: excitement for innovation, skepticism, ethical concerns, or fear of creative loss.

Objectives

  • Analyze Sentiment: Detect and categorize sentiment as positive, neutral, or negative.
  • Understand Emotions: Identify deeper emotional responses, such as trust, fear, anticipation, or joy.
  • Visualize Patterns & Trends: Plot sentiment and emotion trends over time across platforms.
  • Highlight Platform Differences: Compare how AI discussions vary across Reddit, YouTube, and Twitter.

Tools and Libraries

  • Languages: Python
  • Libraries: Pandas, NumPy, Matplotlib, Seaborn, WordCloud
  • NLP Tools: TextBlob, Hugging Face Transformers, NRCLex
  • Data Collection: Reddit API, YouTube API, Twitter API
  • Visualization: Matplotlib, Seaborn, WordCloud

Methodology

1. Data Collection

Data was collected using platform-specific APIs and scraping tools:

  • Reddit: Subreddits discussing AI and creativity (e.g., r/Filmmakers, r/Screenwriting).
  • YouTube: Comments from videos addressing AI tools for filmmaking and media production.
  • Twitter/X: Tweets containing keywords AI movies.

2. Data Cleaning & Preprocessing

  • Removed URLs, special characters, and irrelevant text.
  • Standardized text (lowercase, whitespace removal).
  • Parsed time data for temporal analysis.
  • Handled missing values and duplicates.

3. Sentiment Analysis

Two approaches were used for a comprehensive view:

  • TextBlob: A rule-based tool for broad sentiment classification (polarity and subjectivity).
  • Hugging Face Transformers: A deep learning model for nuanced sentiment detection.

4. Emotion Analysis

Using NRCLex, emotions like joy, trust, fear, and anger were identified to explore deeper perceptions.

5. Visualizations

Key visualizations include:

  • Scatterplots: Sentiment trends over time.
  • Emotion Bar Charts: Breakdown of emotions across platforms.
  • Word Clouds: Common words and themes in public discussions.

Results

Sentiment Analysis

  • YouTube: Exhibited the highest positive sentiment driven by trust and joy. Viewers expressed excitement for AI tools and their applications.
  • Reddit: Discussions revealed higher skepticism and fear, focused on ethical concerns and the threat to human creativity.
  • Twitter/X: Sentiment was largely neutral, reflecting broader, surface-level opinions.

Emotion Analysis

  • Positive Emotions: Trust, anticipation, and joy dominated across all platforms, indicating optimism for AI's potential.
  • Negative Emotions: Fear and anger appeared prominently on Reddit, highlighting polarized discussions and concerns about AI's societal impact.

Key Insights

  • YouTube audiences engage positively, reflecting enthusiasm for AI's practical uses.
  • Reddit serves as a platform for critical discussions, where fear and ethical concerns dominate.
  • Twitter provides a snapshot of generalized discourse, with lower emotional intensity.

Key Visualizations

  • Emotion Distribution: Allows you to see dominant emotions and compare by platform. output_75_0

  • Sentiment Polarity Over Time (TextBlob): Shows sentiment polarity for each platform. output_64_1

  • Sentiment Proportions (Hugging Face Transformers Labeling): Shows sentiment polarity for each platform. output_65_0

To view more visualizations, access the "visuals" folder in the repository files.

Challenges

  • Data Imbalance: Twitter data was limited to 100 tweets due to API restrictions.
  • Model Variance: TextBlob and Transformers provided differing results, requiring deeper emotion analysis to resolve conflicts.
  • Platform Differences: Tailoring preprocessing and analysis techniques to each platform's data format.

Future Work

  • Expand Dataset: Include more platforms (e.g., Instagram or Threads) for a broader analysis.
  • Topic Modeling: Use tools like LDA to identify recurring themes in AI discussions.
  • Temporal Analysis: Analyze long-term trends to track changing public perceptions.
  • Geographic Insights: Explore regional variations in AI sentiment and emotions.