PoliBERT Sentiment Analysis#

Political sentiment analysis skill powered by PoliBERTweet - a transformer model trained on 83 million political tweets (Georgetown University, LREC 2022).

Overview#

This skill provides political sentiment analysis capabilities using a specialized NLP model trained on political content. It can analyze sentiment toward political candidates, issues, and events from various data sources including Reddit, local files, or direct text input.

Features#

• Sentiment Classification: Support / Oppose / Neutral toward political targets
• Stance Detection: Issue-specific stance analysis (e.g., pro/anti immigration)
• Entity Targeting: Analyze sentiment toward specific politicians
• Confidence Scoring: Probability scores for each classification
• Reddit Data Integration: Auto-fetch political discussions from Reddit (free, read-only)
• Batch Processing: Analyze multiple texts from files or stdin
• JSON Output: Machine-readable results for integration with other tools

When to Use#

Use this skill when you need to:

• Analyze public sentiment toward political candidates or figures
• Track political opinion trends on social media
• Complement prediction market data with social sentiment
• Monitor political discourse around specific issues
• Aggregate opinions from Reddit political communities

Model Information#

• Model: PoliBERTweet
• Architecture: RoBERTa (Robustly Optimized BERT)
• Training Data: 83 million political tweets (2016-2020 US elections)
• HuggingFace Hub: kornosk/polibertweet-political-twitter-roberta-mlm
• Model Size: ~500MB
• Academic Paper: LREC 2022
• Institution: Georgetown University DataLab

Installation#

Prerequisites#

bash

# Python 3.9 or higher
python --version

# Install core dependencies
pip install transformers>=4.18.0 torch>=1.10.2

# Optional: Reddit data fetching
pip install praw>=7.8.1

First Run#

On first execution, the model will be automatically downloaded from HuggingFace Hub (~500MB):

bash

python polibert_sentiment.py --text "Test"

Data Sources#

SourceMethodCostData QualityUse CaseReddit--redditFreeHighReal-time political discussionsLocal File--file-User-dependentBatch analysis of collected dataStdin--stdin-User-dependentPipeline integrationDirect Text--text-User-dependentQuick testing and single analysis

Reddit Data#

Default Subreddits: r/politics, r/Conservative, r/democrats, r/Republican, r/PoliticalDiscussion

Note: Reddit data fetching uses read-only mode (no API credentials required). Rate limits apply.

Usage Examples#

1. Single Text Analysis#

bash

python polibert_sentiment.py --text "J.D. Vance is the future of the Republican party"

Output:

text

Text: J.D. Vance is the future of the Republican party
Sentiment: SUPPORT (78.3% confidence)

2. Reddit Sentiment Analysis#

bash

# Analyze J.D. Vance sentiment from Reddit
python polibert_sentiment.py --candidate "J.D. Vance" --reddit --limit 50

# Analyze specific query
python polibert_sentiment.py --query "2028 election" --reddit --limit 100

# Custom subreddits
python polibert_sentiment.py --query "climate policy" --reddit --subreddits politics,environment

3. Batch File Analysis#

bash

# File with one text per line
python polibert_sentiment.py --candidate "Trump" --file tweets.txt

4. JSON Output (for integration)#

bash

python polibert_sentiment.py --candidate "Biden" --reddit --json

Output:

json

{
  "candidate": "Biden",
  "total_analyzed": 47,
  "sentiment_breakdown": {
    "support": {"count": 15, "percentage": 31.9},
    "oppose": {"count": 22, "percentage": 46.8},
    "neutral": {"count": 10, "percentage": 21.3}
  },
  "net_sentiment": -14.9,
  "average_confidence": 72.4
}

Integration with Other Skills#

With Polymarket#

text

Polymarket (market odds)  →  PoliBERT (social sentiment)  →  Prediction synthesis
     18.6% (Vance)                    35% Support                      Combined signal

With Prediction Skill#

Use PoliBERT sentiment as an input factor in the BRACE forecasting framework:

• Base rate: Historical election patterns
• Sentiment: Social media trends (via PoliBERT)
• Market: Prediction market odds (via Polymarket)

Example Workflow#

bash

# 1. Get market data
python polymarket.py search "presidential election winner 2028" --json

# 2. Get social sentiment
python polibert_sentiment.py --candidate "J.D. Vance" --reddit --limit 100 --json

# 3. Synthesize in prediction framework
# (Use prediction skill to combine signals)

Output Format#

Human-Readable Output#

text

📊 Sentiment Analysis: J.D. Vance
Source: Reddit | Total analyzed: 47

Support: 31.9% (15)
Oppose: 46.8% (22)
Neutral: 21.3% (10)

Net Sentiment: -14.9%
Avg Confidence: 72.4%

JSON Output Structure#

json

{
  "candidate": "string",
  "total_analyzed": "integer",
  "sentiment_breakdown": {
    "support": {"count": "integer", "percentage": "float"},
    "oppose": {"count": "integer", "percentage": "float"},
    "neutral": {"count": "integer", "percentage": "float"}
  },
  "average_confidence": "float",
  "net_sentiment": "float",
  "sample_results": [
    {"text": "string", "sentiment": "string", "confidence": "float"}
  ]
}

Limitations and Considerations#

Model Limitations#

1. Training Data: Model trained on 2016-2020 tweets, may not capture 2024-2028 linguistic patterns
2. Context Sensitivity: May miss sarcasm, irony, or cultural references
3. Temporal Drift: Political language evolves; model accuracy may degrade over time
4. Confidence Calibration: Confidence scores are model outputs, not calibrated probabilities

Data Limitations#

1. Reddit Sample Bias: Reddit users skew younger, more educated, more liberal than general population
2. Selection Bias: Active Reddit users are not representative voters
3. Timing: Social sentiment can shift rapidly; snapshot may not represent election day mood
4. Volume: Low-liquidity markets may have few social media discussions

Best Practices#

• Use as one input among many, not sole prediction basis
• Combine with prediction markets, polling data, economic indicators
• Track sentiment trends over time, not single snapshots
• Adjust for platform demographics (Reddit ≠ Twitter ≠ general population)

Citation#

If you use this skill or PoliBERTweet model in research, please cite:

bibtex

@inproceedings{kawintiranon2022polibertweet,
  title={{P}oli{BERT}weet: A Pre-trained Language Model for Analyzing Political Content on {T}witter},
  author={Kawintiranon, Kornraphop and Singh, Lisa},
  booktitle={Proceedings of the Language Resources and Evaluation Conference (LREC)},
  year={2022},
  pages={7360--7367},
  publisher={European Language Resources Association}
}

License#

• Skill Code: MIT License
• PoliBERTweet Model: Subject to HuggingFace Hub and original paper terms

Feedback and Contributions#

• Report issues: Create GitHub issue
• Model questions: See PoliBERTweet repository

Related Skills#

• polymarket-unified - Prediction market data for political forecasting
• prediction - BRACE framework for calibrated forecasting
• ai-model-team - Multi-model prediction system for financial markets

Version History#

• v1.0.0 (2026-04-17): Initial release

  • PoliBERTweet model integration
  • Reddit data source support
  • Sentiment analysis pipeline
  • JSON and human-readable output formats
  • Batch processing capabilities