Abstract:

This study explores emotion-laden items in a corpus of financial reports in Spanish and their English translations. Firstly, an analysis of lexical frequencies was carried out, followed by processing through Lingmotif SA software to diagnose sentiment intensity and polarity in the source and target texts. Finally, the emotion-laden items in both versions were classified within the framework of Appraisal Theory, to further organize positive and negative items into categories. Overall, our results show how sentiment intensity and polarity are generally maintained in both versions, whereas the way in which these are expressed varies in each version for functional reasons.

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