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Subtitling Emotion Words Between English and Arabic: A Corpus-Based Study of Collocational Patterns
Subtitling Emotion Words Between English and Arabic: A Corpus-Based Study of Collocational Patterns
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Subtitling Emotion Words Between English and Arabic: A Corpus-Based Study of Collocational Patterns
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Subtitling Emotion Words Between English and Arabic: A Corpus-Based Study of Collocational Patterns
Subtitling Emotion Words Between English and Arabic: A Corpus-Based Study of Collocational Patterns

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Subtitling Emotion Words Between English and Arabic: A Corpus-Based Study of Collocational Patterns
Subtitling Emotion Words Between English and Arabic: A Corpus-Based Study of Collocational Patterns
Journal Article

Subtitling Emotion Words Between English and Arabic: A Corpus-Based Study of Collocational Patterns

2025
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Overview
This study compares verb collocations with emotion words related to happiness and anger in English and Arabic subtitles using Sketch Engine's corpus and Word Sketch functionality. The research employs a mixed-methods approach, combining quantitative analysis of LogDice statistics and qualitative examination of subtitle samples. The study aims to identify differences in verb collocations between the two languages, explore patterns and translation strategies in subtitling, and evaluate Sketch Engine's effectiveness in facilitating statistical analysis for translation research. The study reveals a prevalent use of literal translation in subtitling, which often results in unnatural expressions in the target language, mainly when dealing with emotion-related collocations that differ between English and Arabic. The research underscores the significance of corpus-based methodologies in translation studies and demonstrates Sketch Engine's efficacy in identifying linguistic patterns and collocations across different contexts. This study contributes valuable insights for enhancing subtitle translation efficiency and cultural appropriateness while deepening the understanding of linguistic and cultural nuances in subtitling emotional expressions.