NeuEmot: Mitigating Neutral Label and Reclassifying False Neutrals in the 2022 FIFA World Cup via Low-Level Emotion

Sports have been extensively studied for their impact on people’s emotional well-being, with research revealing that they have the ability to reduce anxiety and unhappiness while boosting positive emotions
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. Among all sports, soccer stands out as the most popular and controversial
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, eliciting a wide range of emotional reactions from fans, players, officials, and spectators, particularly on social media. While sentiment classifications such as positive, negative, and neutral have been extensively studied, low-level emotions, which refer to more specific and granular emotional states beyond the three basic categories, have yet to be given much attention. This study scraped over 300,000 tweets during the 2022 FIFA World Cup in Qatar to gain a more in-depth understanding of human emotions. We use the dataset to finetune transformer models such as pre-trained BERT and RoBERTa to detect high and low-level emotions in the collected tweets. Pre-trained $\mathrm{RoBERTa}_{\mathrm{Sent3}}$ was initially used to predict three high-level classes. Three models were then trained using one model with 28 and two models with 27 GoEmotions labels from Reddit comments to break the high-level sentiment into low-level emotions. Impressively, these models outperformed GoEmotions paper classification research in terms of precision and F1-score for BER$\mathrm{T}_{\mathrm{Emot}28}$, and they predicted less neutral labels compared to $\mathrm{RoBERTa}_{\mathrm{Sent3}}$. To further improve the classification accuracy, BER$\mathrm{T}_{\mathrm{Emot}27}$ and RoBERT$\mathrm{a}_{\mathrm{Emot}27}$ were used to reclassify BER$\mathrm{T}_{\mathrm{Emot}28}$, and these models achieved better performance in terms of precision, recall, and F1-score compared to other models. BER$\mathrm{T}_{\mathrm{Emot}27}$ was particularly effective in eliminating false neutral classifications, reclassifying the predicted neutral label class from BER$\mathrm{T}_{\mathrm{Emot}28}$ into other classes and achieving retrieval counts of 50%, 51%, 53%, and 53% for weeks 1, 2, 3, and 4 respectively. This study stresses the importance of using a nuanced approach to emotion classification and provides valuable insights into the emotional landscape of a major sporting event. It demonstrates that tweets initially perceived as neutral contained other emotional content, highlighting the need for further research.
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Mental Health Benefits of Sports
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The Most Popular Sports In The World