From Niche to Mainstream: Anime Sentiment Evolution on Reddit 🎌

Tracking how Reddit's perception of Japanese anime evolved from "weird hobby" to box office dominator over 18 years

Spoiler: The weebs were right all along 🎌

View Analysis • Explore Notebooks • Getting Started

---

🎯 Project Overview

Remember when liking anime meant you were automatically labeled "weird" or "nerdy"? This comprehensive NLP project investigates how public sentiment toward Japanese anime has transformed from 2007 to 2025 using Reddit as our data source.

What We Discovered:

• ✅ 66% positive sentiment across 20,000 analyzed discussions
• ✅ +0.333 point sentiment improvement from 2007 to 2025
• ✅ 6,954% increase in discussion volume (9 posts → 3,477 posts)
• ✅ 2016-2019 identified as the mainstream breakthrough period
• ✅ 2025 shows all-time peak sentiment (0.272) - still ascending!

Research Questions Answered

Question	Answer	See Details
Has sentiment improved over time?	✅ YES - Statistically significant (+16.3%, p<0.0001)	Notebook 02
When did mainstream shift occur?	📍 2016-2019 (Your Name era)	Notebook 03
How do communities differ?	🎯 Streaming (0.446) > Mainstream (0.22) > Enthusiasts (0.218)	Notebook 02
Is the trend sustainable?	✅ YES - 2025 shows highest sentiment ever	Notebook 03

---

📊 Key Findings

Overall Sentiment Distribution

Positive: 13,191 submissions (66.0%) 🟢
Neutral:   3,158 submissions (15.8%) ⚪
Negative:  3,651 submissions (18.3%) 🔴

Mean Score: 0.237 (Moderately Positive)
Positive-to-Negative Ratio: 3.6:1

→ See detailed breakdown in Notebook 02: Sentiment Analysis

Sentiment Evolution Timeline

Era	Years	Mean Sentiment	Key Events
Underground	2007-2009	-0.06 to +0.09	Negative → Neutral transition
Foundation	2010-2013	+0.20	First sustained positive sentiment
Breakthrough	2014-2017	+0.22	Your Name (2016) crossover success
Mainstream	2018-2021	+0.24	Demon Slayer, pandemic boom
Maturity	2022-2025	+0.27	All-time peak, normalized acceptance

→ See temporal visualizations in Notebook 03: Temporal Trends

Community Rankings

Rank	Subreddit	Sentiment	Community Type
🥇	r/streaming	0.446	Platform-native
🥈	r/MovieSuggestions	0.385	Discovery/Recommendations
🥉	r/cinematography	0.373	Artistic appreciation
4	r/TrueFilm	0.258	Critical analysis
5	r/movies	0.226	General audience
6	r/television	0.223	TV/Series focus
7	r/anime	0.218	Enthusiast community
8	r/netflix	0.199	Platform-specific
9	r/boxoffice	0.172	Business-focused
10	r/entertainment	0.069	General entertainment

→ See community analysis in Notebook 02: Community Sentiment

Major Release Impacts

Top Positive Impact:

1. Your Name (+0.123) - Exceptional crossover success
2. The Boy and the Heron (+0.051) - Recent critical acclaim
3. Demon Slayer: Mugen Train (+0.048) - Box office phenomenon

→ See release impact analysis in Notebook 03: Major Releases

---

🏗️ Project Structure

from-niche-to-mainstream/
├── scripts/                    # Console scripts for automation
│   ├── scraper.py             # Reddit data collection via PRAW
│   ├── preprocessor.py        # Text cleaning & normalization
│   ├── config.py              # Project configuration
│   └── utils.py               # Helper functions
│
├── data/                       # Data storage (git-ignored)
│   ├── raw/                   # Raw scraped data (43,710 submissions)
│   ├── processed/             # Cleaned & processed data (20,000 sample)
│   └── results/               # Analysis outputs & visualizations
│
├── notebooks/                  # Jupyter notebooks for analysis
│   ├── 01_data_exploration.ipynb          # Dataset validation & EDA
│   ├── 02_sentiment_analysis.ipynb        # Multi-model sentiment scoring
│   ├── 03_temporal_trends.ipynb           # Time-series & turning points
│   └── 04_visualizations.ipynb            # Final dashboards & charts
│
├── requirements.txt            # Python dependencies
├── .env.example               # Environment variables template
└── README.md                  # This file

---

📚 Notebooks Guide

01 - Data Exploration

Focus: Dataset validation, quality assessment, and initial exploration

Key Sections:

• Dataset overview (43,710 submissions, 18 years, 10 subreddits)
• Data quality report (100/100 quality score)
• Temporal coverage analysis
• Community distribution breakdown

Technologies: pandas, numpy, matplotlib, seaborn

02 - Sentiment Analysis

Focus: Multi-model sentiment scoring and community-level analysis

Key Sections:

• Ensemble sentiment analysis (VADER + TextBlob + BERT)
• Overall sentiment distribution (66% positive)
• Community sentiment rankings
• Model performance comparison
• Statistical validation (t-tests, ANOVA)

Technologies: vaderSentiment, textblob, transformers (BERT), scipy

Key Visualizations:

• Sentiment distribution histogram
• Community sentiment rankings
• Box plots by subreddit

03 - Temporal Trends

Focus: Time-series analysis, turning points, and release impacts

Key Sections:

• Year-over-year sentiment changes
• Identification of 3 major turning points (2008, 2010, 2012)
• Major anime release impact analysis
• Period-based evolution (4 distinct phases)
• Mainstream penetration ratio tracking
• Sentiment-engagement correlation analysis

Technologies: pandas, scipy, statsmodels

Key Visualizations:

• Quarterly sentiment trends (with confidence intervals)
• Release impact comparison
• Subreddit evolution heatmap
• Mainstream penetration ratio timeline

04 - Visualizations

Focus: Comprehensive dashboards and publication-ready charts

Key Sections:

• Three-panel journey visualization (sentiment + volume + engagement)
• Subreddit sentiment heatmap (18 years × 10 communities)
• Word cloud evolution (early vs. recent discourse)
• Sentiment distribution box plots
• Keyword frequency & sentiment analysis
• Integrated dashboard (6-panel synthesis)

Technologies: matplotlib, seaborn, wordcloud, plotly

Key Visualizations:

• Journey Timeline: See notebook section
• Heatmap: See notebook section
• Word Clouds: See notebook section
• Dashboard: See notebook section

---

🚀 Getting Started

Prerequisites

• Python 3.11+ (tested on 3.11.5)
• Reddit API credentials (Get them here)
• Virtual environment (recommended)
• 8GB RAM minimum (for BERT model)

Installation

1. Clone the repository

git clone https://github.com/hasandafa/from-niche-to-mainstream.git
cd from-niche-to-mainstream

2. Create and activate virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Download NLTK data (required for preprocessing)

python -c "import nltk; nltk.download('vader_lexicon'); nltk.download('punkt'); nltk.download('stopwords')"

5. Set up environment variables

# Copy example file
cp .env.example .env

# Edit .env with your credentials
REDDIT_CLIENT_ID=your_client_id_here
REDDIT_CLIENT_SECRET=your_client_secret_here
REDDIT_USER_AGENT=anime-sentiment-analyzer/1.0

Quick Start

Option 1: Run Full Pipeline

# Scrape data (may take several hours)
python scripts/scraper.py --all --time-filter all

# Preprocess data
python scripts/preprocessor.py --input data/raw/ --output data/processed/

# Launch Jupyter to run analysis
jupyter notebook notebooks/

Option 2: Use Pre-collected Data

# If you have the processed dataset, skip directly to analysis
jupyter notebook notebooks/02_sentiment_analysis.ipynb

---

🔬 Methodology

Data Collection Strategy

Target Subreddits (10 communities):

• General: r/movies, r/television, r/entertainment
• Film-focused: r/TrueFilm, r/boxoffice, r/cinematography
• Streaming: r/netflix, r/streaming
• Discovery: r/MovieSuggestions
• Dedicated: r/anime

Timeline Coverage:

• 2007-2009: Pre-mainstream era (underground/niche)
• 2010-2015: Foundation period (early adoption)
• 2016-2019: Breakthrough era (mainstream crossover)
• 2020-2025: Modern era (normalized acceptance)

Filtering Criteria: Posts/comments mentioning:

• Generic terms: "anime", "japanese animation", "manga"
• Popular titles: "Your Name", "Demon Slayer", "Attack on Titan", "One Piece", "Spirited Away"
• Studios/creators: "Studio Ghibli", "Makoto Shinkai", "Hayao Miyazaki"

→ See complete filtering logic in scraper.py

Sentiment Analysis Pipeline

Three-Model Ensemble Approach:

1. VADER (Valence Aware Dictionary and sEntiment Reasoner)

   • Rule-based lexicon approach
   • Optimized for social media text
   • Handles slang, emoji, and intensifiers
   • Weight: 33.3%

2. TextBlob

   • Pattern-based sentiment analysis
   • Good at detecting polarity and subjectivity
   • Sensitive to negative language patterns
   • Weight: 33.3%

3. BERT (Bidirectional Encoder Representations from Transformers)

   • Deep learning transformer model
   • Context-aware sentiment understanding
   • Handles nuanced and sarcastic language
   • Weight: 33.3%

Ensemble Formula:

ensemble_score = (vader_score + textblob_score + bert_score) / 3

Classification Thresholds:

• Positive: score > 0.05
• Neutral: -0.05 ≤ score ≤ 0.05
• Negative: score < -0.05

→ See implementation details in Notebook 02

Statistical Validation

Tests Applied:

• Independent t-tests: Compare sentiment across time periods
• ANOVA: Test for significant differences across communities
• Pearson correlation: Measure sentiment-engagement relationships
• Time series analysis: Identify trends and turning points

Significance Level: α = 0.05 (95% confidence)

→ See all statistical tests in Notebook 03

---

📈 Key Insights

1. The "Enthusiast Paradox"

Finding: r/anime shows lower sentiment (0.218) than mainstream subreddits (0.226-0.446)

Why: Enthusiast communities maintain higher critical standards and engage in deeper analysis, resulting in more nuanced (lower) sentiment scores. This is actually a positive indicator of mature, thoughtful discourse.

→ See analysis in Notebook 02: Community Insights

2. The "Streaming Revolution"

Finding: r/streaming shows highest sentiment (0.446) of all communities

Why: Platform accessibility through Netflix, Crunchyroll, and others has normalized anime consumption, removing stigma and making discovery easier. Modern viewers encounter anime as standard content, not as niche curiosity.

→ See streaming impact in Notebook 02: Platform Effect

3. The "Your Name Effect"

Finding: Your Name (2016) shows largest positive sentiment impact (+0.123) of any release

Why: Theatrical releases with broad crossover appeal drive mainstream sentiment more effectively than series content. Your Name set the benchmark for anime's theatrical success in Western markets.

→ See release comparison in Notebook 03: Major Releases

4. The "Normalization Paradox"

Finding: Non-anime subreddit engagement declined 80% (2018-2025) while sentiment increased

Why: This is not a negative trend. It reflects successful cultural integration—anime is no longer a "special topic" requiring mainstream forum discussion. Dedicated platforms and communities now handle most discourse, while mainstream audiences treat anime as standard entertainment.

→ See engagement analysis in Notebook 03: Normalization Pattern

5. Discourse Evolution

Early Period (2010-2015): Focus on "film", "people", "know", "think" - justification and comparison

Recent Period (2020-2025): Focus on "movies", "link", "scene", "character", "good", "love" - celebration and recommendation

Linguistic Shift:

• "Film" (singular) → "Movies" (plural) = exceptional art → diverse category
• "People" → "Character" = audience focus → content focus
• "Know"/"Think" → "Want"/"Need" = uncertainty → enthusiasm

→ See word clouds in Notebook 04: Discourse Evolution

---

📊 Usage Examples

Analyze Specific Time Period

import pandas as pd
from scripts.utils import load_data, filter_by_date

# Load processed data
df = load_data('data/processed/sentiment_scored.csv')

# Filter to breakthrough period
breakthrough = filter_by_date(df, start='2016-01-01', end='2019-12-31')

# Calculate average sentiment
avg_sentiment = breakthrough['ensemble_score'].mean()
print(f"Breakthrough Period Sentiment: {avg_sentiment:.3f}")

Compare Communities

from scripts.utils import compare_communities

# Load data
df = load_data('data/processed/sentiment_scored.csv')

# Compare streaming vs anime subreddit
comparison = compare_communities(df, 
                                 communities=['streaming', 'anime'],
                                 metric='ensemble_score')

print(comparison)
# Output: Statistical comparison with t-test results

Track Keyword Sentiment

from scripts.utils import keyword_sentiment_tracking

# Load data
df = load_data('data/processed/sentiment_scored.csv')

# Track "Your Name" sentiment over time
your_name_trend = keyword_sentiment_tracking(df, 
                                              keyword='Your Name',
                                              time_window='quarterly')

# Visualize
your_name_trend.plot(kind='line', title='Your Name Sentiment Tracking')

→ See more examples in utils.py

---

🎨 Visualizations Gallery

Journey Visualization (2007-2025)

Three-panel view showing sentiment evolution, volume growth, and engagement trends

→ Generate this viz

Subreddit Sentiment Heatmap

18 years × 10 communities showing temporal sentiment evolution by subreddit

→ Generate this viz

Word Cloud Evolution

Side-by-side comparison of early (2010-2015) vs recent (2020-2025) discourse vocabulary

→ Generate this viz

Comprehensive Dashboard

Six-panel integrated analysis showing all key metrics in one view

→ Generate this viz

---

🔧 Technical Details

Performance Optimization

Data Processing:

• Batch processing for large datasets (chunking by 10,000 rows)
• Multiprocessing for VADER and TextBlob (CPU-bound)
• GPU acceleration for BERT (when available)
• Caching of preprocessed text

Memory Management:

• Stratified sampling for 20,000 subset analysis
• Lazy loading of large CSV files
• Garbage collection after heavy operations

Model Specifications

VADER:

• Library: vaderSentiment 3.3.2
• No training required (lexicon-based)
• Processing time: ~0.001s per text

TextBlob:

• Library: textblob 0.17.1
• No training required (pattern-based)
• Processing time: ~0.002s per text

BERT:

• Model: distilbert-base-uncased-finetuned-sst-2-english
• Library: transformers 4.30+
• Processing time: ~0.1s per text (GPU) / ~0.5s per text (CPU)
• GPU memory: ~2GB

Reproducibility

Random Seeds:

RANDOM_SEED = 42
np.random.seed(RANDOM_SEED)
torch.manual_seed(RANDOM_SEED)

→ See full requirements in requirements.txt

---

---

📝 Read the Full Story

🔥 Featured Article on Substack:

"Remember When Liking Anime Made You 'Weird'? Yeah, About That..."

A 5,000-word data-driven deep dive into anime's complete cultural takeover

Article Highlights:

• 📊 The Numbers Don't Lie - 66% positive sentiment, 6,954% growth
• 🎭 The Enthusiast Paradox - Why hardcore fans are more critical
• 🎬 Your Name Effect - The movie that changed everything
• 📱 The Streaming Revolution - How Netflix normalized anime
• 💬 Discourse Evolution - From justification to celebration
• 🔮 Future Trajectory - Where anime goes from here

Visual Journey: The article features 10 custom visualizations from this analysis, including sentiment heatmaps, temporal trends, word clouds, and the comprehensive dashboard. Each chart tells part of the story of anime's cultural transformation.

Read Time: 18-22 minutes

---

📦 Resources

📊 Dataset (Kaggle)

Anime Sentiment Data From Reddit

• 43,710 submissions with sentiment scores
• 18 years of temporal data (2007-2025)
• Multi-model ensemble analysis (VADER + TextBlob + BERT)
• Ready for your own analysis

📝 Article (Substack)

Remember When Liking Anime Made You 'Weird'?

• Full narrative analysis with data storytelling
• Custom visualizations and insights
• Discussion of cultural implications

💻 Code (GitHub)

This Repository

• Complete analysis notebooks
• Data collection scripts
• Visualization code
• Reproducible research

📝 Citation

If you use this analysis in your research, please cite:

@misc{from-niche-to-mainstream,
  author = {Abdullah Hasan Dafa},
  title = {From Niche to Mainstream: Anime Sentiment Evolution on Reddit (2007-2025)},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/hasandafa/from-niche-to-mainstream}
}

---

🤝 Contributing

This is currently a personal research project, but contributions are welcome!

How to contribute:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-analysis)
3. Commit your changes (git commit -m 'Add amazing analysis')
4. Push to the branch (git push origin feature/amazing-analysis)
5. Open a Pull Request

Areas for contribution:

• Additional sentiment models (RoBERTa, XLNet)
• Alternative data sources (Twitter, YouTube comments)
• Extended temporal coverage (pre-2007 if data available)
• Advanced visualizations (interactive Plotly dashboards)
• Comparative analysis (anime vs. other media forms)

---

📄 License

MIT License - See LICENSE file for details

TL;DR: Free to use, modify, and distribute with attribution.

---

🙏 Acknowledgments

Data Sources:

• Reddit API via PRAW
• Pushshift for historical data access

NLP Libraries:

• VADER Sentiment by C.J. Hutto
• TextBlob by Steven Loria
• Hugging Face Transformers for BERT models

Community:

• All the anime fans whose passionate discussions made this analysis possible
• Reddit moderators who maintain these communities
• The open-source NLP community

Special Thanks:

• The creators who made anime worth talking about
• The platforms (Netflix, Crunchyroll) that normalized anime consumption
• Everyone who's ever recommended anime to a friend

---

📞 Contact

Author: Abdullah Hasan Dafa Email: dafa.abdullahhasan@gmail.com
GitHub: @hasandafa
LinkedIn: Abdullah Hasan Dafa

Project Link: https://github.com/hasandafa/from-niche-to-mainstream

---

⭐ If you found this analysis interesting, please star the repository! ⭐

📊 Total Analysis: 43,710 submissions | 18 years | 10 communities | 3 NLP models

🎯 Final Verdict: Anime has successfully transitioned from niche to mainstream. The data proves it.

Last updated: October 2025