Search Results for « Emotions »

This paper argues that everyday argumentative episodes are inherently emotional and proposes applying the concepts and methods used to analyze emotional episodes to argumentative-emotional ones. Emotions develop at the verbal and physical (paraverbal) levels. Sections one and two discuss two paraverbal aspects of emotion. The first section presents increased tension as a criterion for identifying the beginning of an emotional-argumentative episode. Section two focuses on the physical transformations individuals experience during emotional interactions and their correlative communicative and argumentative function. This topic is difficult to document adequately and the data are difficult to interpret. It constitutes an autonomous field of study. Sections three through five focus on the lexicon of emotional terms. This lexicon includes hundreds of terms that refer to emotions. In addition to this core set, it includes some thousands of terms that suggest, connote, imply, or point toward an emotion (some thousands). The dictionary-based impact method is a unifying approach to an expanded set of emotional terms.

In the reported analysis, we investigate how different automated translation methods available for the Polish-English language pair render emotional content in English. Using emotion and sentiment analysis, 206 English translations of Polish reviews from Booking.com were examined for the frequency of words related to basic emotions and respective sentiment scores. Correspondence analysis revealed clear distinctions between AI-generated and machine translations. Regression modelling confirmed that GPT-3.5’s sentiment scores align more closely with original review ratings, while DeepL Translate and Google Translate exhibit flatter sentiment curves. Notably, emotions with fewer Polish-English equivalents (like anger or disgust) are translated less consistently across tools, reinforcing prior findings on emotional non-equivalence. Emotions like joy, with high equivalence, are rendered more uniformly. The findings suggest GPT-3.5’s superior sensitivity to emotional tone, with implications for translation practice and training, especially in emotionally charged content.