Segmentation of Weather News Headlines: Sentiment and Tone Shifts in Parallel Bilingual Corpora

dc.contributor.author	Marchuk, Oksana
dc.date.accessioned	2024-05-14T16:19:53Z
dc.date.available	2024-05-14T16:19:53Z
dc.date.issued	2023-10
dc.identifier.citation	https://ieeexplore.ieee.org/document/10324281	uk_UA
dc.identifier.issn	979-8-3503-6046-2
dc.identifier.uri	https://archer.chnu.edu.ua/xmlui/handle/123456789/10079
dc.description.abstract	This paper explores whether emotional tone/sentiment of the text is completely/partially lost, shifted or preserved in parallel bilingual corpora of weather news headlines and to what extent the translation techniques chosen influence this process. To this aim we have carried out both sentiment and tone analysis of weather news headlines, compared and critically evaluated the obtained results. For investigating the influence of translation techniques used on the sentiment/tone of the expression two programs were applied: The IBM Watson™ Tone Analyzer and MonkeyLearn. The first program is used for defining the tone of the text (in our work ˗ the headline), and the second points out its sentiment.	uk_UA
dc.language.iso	en	uk_UA
dc.publisher	IEEE	uk_UA
dc.subject	Surveys;Training;Computer science;Correlation;Information	uk_UA
dc.subject	Surveys, Training, Computer science, Correlation, Information technology, Meteorology	uk_UA
dc.title	Segmentation of Weather News Headlines: Sentiment and Tone Shifts in Parallel Bilingual Corpora	uk_UA
dc.type	Working Paper	uk_UA