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Interpretation of emotions from text on social media
Tlustoš, Vít ; Košař, Vlastimil (oponent) ; Malik, Aamir Saeed (vedoucí práce)
Most human interactions are either text-based or can be converted to text using speech-to-text technologies. This thesis is dedicated to recognizing emotions from these texts. Despite extensive research in this domain, three significant challenges persisted: unexplored or limited cross-domain efficacy of the methods, superficial analysis of the result, and limited usability of the outcomes. We address these challenges by proposing two models based on the RoBERTa model, which we call EmoMosaic-base and EmoMosaic-large. These models were trained on the following datasets: SemEval-2018 Task 1: Affect in Tweets, GoEmotions, XED, and DailyDialog datasets. In contrast to prior studies, we trained our models on all the datasets simultaneously while preserving their original categories. This resulted in models that exhibit strong performance across diverse domains and are directly comparable to other methods. In fact, EmoMosaic-large outperforms recent single-domain state-of-the-art models on SemEval-2018 Task 1: Affect in Tweets and GoEmotions datasets, demonstrating outstanding cross-domain performance. To promote the usability and reproducibility of our research, we make all our code and models public, available at: https://huggingface.co/vtlustos.
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