Beyond The Score — Football Sentiment Analysis

A machine learning project that analyses public opinion about football players using Reddit, Twitter, and ESPN data. Classifies sentiment as Positive, Negative, or Neutral using NLP and 4 trained models, served through a live Flask web application.

Scrapes public comments and news about football players, cleans and processes the text, trains ML models to classify sentiment, and displays everything in a dashboard with live prediction.

Can you type any text about a player and instantly know if public opinion is positive, negative, or neutral? Yes — that's exactly what the live prediction feature does.

Best model: Random Forest — 89.9% accuracy on 42,948 unseen records

git clone https://github.com/manny2341/Football-Sentiment-Analysis-main.git
cd Football-Sentiment-Analysis-main
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

python3 cleaning.py

python3 model_training.py

Trains 4 models with 5-fold cross-validation. All models saved to saved_models/.

python3 app.py

Open http://localhost:5001

Football-Sentiment-Analysis-main/
├── app.py                          # Flask web application
├── cleaning.py                     # Text preprocessing pipeline
├── model_training.py               # Train + evaluate all 4 models
├── redditscrape.py                 # Scrape Reddit r/soccer
├── add_new_players.py              # Add new players without re-scraping
├── analyze_reddit.py               # VADER sentiment labelling
├── combine_datasets.py             # Merge Reddit + ESPN data
├── templates/
│   └── index.html                  # Dashboard UI
├── saved_models/                   # Trained models (local only)
│   ├── Logistic_Regression.pkl
│   ├── Linear_SVM.pkl
│   ├── Random_Forest.pkl
│   ├── Gradient_Boosting.pkl
│   ├── tfidf_word.pkl
│   ├── tfidf_char.pkl
│   └── label_encoder.pkl
└── results/                        # Charts (local only)
    ├── model_comparison.png
    ├── confusion_matrices.png
    └── top_features.png

Reddit / ESPN / Twitter
        ↓
redditscrape.py / fetchtweets.py
        ↓
analyze_reddit.py  (VADER sentiment labelling)
        ↓
combine_datasets.py  (merge all sources)
        ↓
cleaning.py  (slang expansion, lemmatisation, stopwords)
        ↓
model_training.py  (TF-IDF 8,000 features → 4 ML models)
        ↓
app.py  (Flask dashboard + live prediction)

python3 add_new_players.py

Scrapes Reddit for new players only, merges with existing data, and re-cleans automatically.

• BERT / RoBERTa transformer model for higher accuracy
• Twitter data re-integration when API access available
• Real-time scraping and automatic model retraining
• Confidence calibration for better probability estimates

Reddit r/soccer — scraped using PRAW (Python Reddit API Wrapper) ESPN Football News — scraped using BeautifulSoup

This project is for educational purposes only. Sentiment scores reflect public Reddit/ESPN commentary and do not represent the views of the project author.

MIT Licence — free to use, modify, and distribute with attribution.