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Automatic Extraction of Soccer Game Event Data from Twitter
This document presents a study on the automation of soccer game event data extraction from Twitter, focusing on detecting, classifying, and assigning events related to goals, cards, and substitutions. The researchers collected data from 61 games and analyzed tweet patterns to identify and classify events, achieving promising results in event classification and team assignment. Future work includes incorporating sentiment analysis and expanding the methodology to other sports.
Automatic Extraction of Soccer Game Event Data from Twitter
- 1. Automa'c extrac'on of soccer game event data from Twi6er Guido van Oorschot, Marieke van Erp and Chris Dijkshoorn Monday, November 12, 12
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- 3. Theory 1. Fair body of research on automated sports highlight extraction 2. Twitter data can offer interesting insights in real world phenomena Monday, November 12, 12
- 4. Automated highlight detec@on Let’s Use Twitter data! Monday, November 12, 12
- 5. 3 Tasks 1. Detecting events What minutes did events occur? 2. Classifying events Is the event a goal, card or substitution? 3. Assigning events to teams Is the event for the home team or away team? Monday, November 12, 12
- 6. 5 types of events - Goal - Own Goal - Red Card - Yellow Card - Substitution Monday, November 12, 12
- 7. Methodology 1. Gathering the data 2. Exploring and cleaning the data 3. Classifying interesting data points Monday, November 12, 12
- 8. Gathering data - Collect all tweets with game hashtags #ajafey #nacgro #psvutr - Collect official data for each match Goals, cards, substitutions Monday, November 12, 12
- 9. Our data 6 months 61 games 661 events 10,643 tweets Monday, November 12, 12
- 10. Three Experiments 1. Detecting events 2. Classifying events 3. Assigning events to teams Monday, November 12, 12
- 11. 1. Detecting events Monday,November 12, 12
- 12. 1. Detecting events Monday,November 12, 12
- 13. 1. Experimental Setup - Goal: detect peaks in # tweets per minute signal to extract events - Setup: Test three peak detection methods: 1. LocMaxNoBaseLineCorr 2. IntThresNoBaseLineCorr 3. IntThresWithBaseLineCorr Monday, November 12, 12
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- 15. 1. Findings - Goals and red cards are detected better than yellow cards and substitutions - None of the three peak selection methods works well. - Highlights can be extracted, but not precise enough Monday, November 12, 12
- 16. Three Experiments 1. Detecting events 2. Classifying events 3. Assigning events to teams Monday, November 12, 12
- 17. 2. Classifying Events - Goal: Classify minutes into event classes minute “goal” “1” “red” “card” “boring” class 34 0 2 0 1 20 nothing 35 23 34 0 0 0 goal 12 1 2 0 0 5 nothing 13 1 0 22 11 0 red card Monday, November 12, 12
- 18. Issues Problem: Huge, sparse matrix 1. Reduce features Choose words/features smartly 2. Reduce instances Choose minutes smartly Monday, November 12, 12
- 19. 2. Experimental Setup - 3 Instance selection settings 1. AllMinutes 2. PeakMinutes 3. Eventminutes Monday, November 12, 12
- 20. 2. Experimental Setup - 7 Feature selection settings 1. AllMoreThanOnce 2. Top500TotalFreq 3. Top10MinuteFreq 4. Top500TotalTfIdf 5. Top10MinuteTfIdf 6. Top50Infogain 7. Top50GainRatio Monday, November 12, 12
- 21. 2. Experimental Setup - 6 types of classifiers 1. C4.5 2. RandomForest 3. NaiveBayes 4. NaiveBayesMultinomial 5. libSVM 6. IB1 Monday, November 12, 12
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- 23. 2. Discussion - Top50GainRatio best feature selection - libSVM best classifier - EventMinutes results: Class F-‐measure OVERALL 0.822 Goal 0.841 Own goal 0.000 Red card 0.848 Yellow card 0.785 Subs@tu@on 0.839 Monday, November 12, 12
- 24. Three Experiments 1. Detecting events 2. Classifying events 3. Assigning events to teams Monday, November 12, 12
- 25. 3. Experimental Setup - Goal: Assign events to team - Based on the ratio between tweets from fans for home and away team - But first: extract fans Monday, November 12, 12
- 26. 3. Extracting fans - Hypothesis: People that tweet for the same team each week are probably fan of that team Monday, November 12, 12
- 27. 3. Extracting fans - Extracted 38,527 fans rom 146,326 f users (26%) - This method of extracting fans works well: Right team Not clear Wrong team 88% 10% 2% Monday, November 12, 12
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- 29. 3. Results - Performance of assigning events to teams above baseline performance: Class Baseline Performance OVERALL 52% 58% Goal 58% 69% Red card 50% 62% Yellow card 63% 63% Subs@tu@on 52% 57% Monday, November 12, 12
- 30. Conclusion 1. Detecting events => difficult 2. Classifying events => good results 3. Assigning events to teams => promising results Monday, November 12, 12
- 31. Future Work - Use sentiment in tweets (for detecting events and assigning events to teams) - Player detection - Other sports Monday, November 12, 12
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