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Contents

Temperature and emotion in English : corpus vs. sentiment-AI analysis / Suwon Yoon ; James Yae 1

[요약] 1

1. Introduction 1

2. The landscape of temperature terms in English 3

2.1. Sentiment of warm and hot 3

2.2. Sentiment of cool and cold 6

2.3. Sentiment of extreme temperature terms : freezing and scalding 8

3. Corpus analysis of sentiment in temperature terms 10

3.1. Collocation trend for temperature terms in English 11

3.2. Analysis : Correlation between temperature and human emotion 13

4. Sentiment AI analysis of temperature terms 17

4.1. What is Sentiment AI? 17

4.2. Sentiment AI analysis of temperature terms 18

4.3. Remaining challenges in Sentiment AI 24

5. Conclusion 24

References 26

초록보기

The current study investigates emotional attitudes of temperature terms in English via corpus analysis and sentiment AI analysis. First, based on the collocation patterns that are extracted from corpora in English (Corpus of Contemporary American English and iWEB), we attempt to specify a potential emotive feature for each temperature term. Second, we compare the results of corpus-based analysis in English with what a Sentiment Artificial Intelligence (Sentiment AI) model predicts. These results reveal how the meaning of temperature terms can be multidimensional: (a) a literal or figurative meaning in the semantic descriptive dimension; and (b) a speaker’s positive or negative emotional attitude in the evaluative dimension. Theoretical implications of the current study include the following: For one thing, we identify temperature terms as another clear case of sentiment-encoded words in English. Further, the current corpus analysis suggests adding to sentiment dictionary the newfound positive or negative connotational differences of temperature terms as a potential feature. Finally, our findings further support the notion of multidimensionality in meaning.

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Temperature and emotion in English : corpus vs. sentiment-AI analysis Suwon Yoon, James Yae p. 603-629

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