Sentiment analysis and classification of Ghanaian football tweets from the 2022 FIFA World Cup
Why this work is in the frame
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Bibliographic record
Abstract
Football as an attractive sport generates huge volumes of tweets concerning fans' opinions, feelings, and judgments during prime events. Such data can be leveraged in sentiment analysis, an algorithmic approach to analyzing text in tweets by extracting emotional tones. This paper presents a novel benchmark dataset of 132,115 tweets collected during the 2022 world cup on 𝕏 (formerly Twitter) for football-related sentiment classification. We also performed sentiment analysis on the dataset using lexicon-based tools, traditional machine learning algorithms, and pre-trained models, robustly optimized bidirectional encoder representations from transformers (BERT)- pretraining approach RoBERTa and distilled version of BERT (DistilBERT) to understand the emotions and reactions of football fans during different phases of the football matches. Results from the study indicate that most tweets had neutral sentiments in both context-aware and context-free analysis. We also describe our novel GhaFootBERT, a sentiment classification model based on transfer learning on BERT, which provides an effective approach to sentiment classification of football-related tweets. Our model performs robustly, outperforming the traditional models with 92% accuracy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it