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Emotion Detection in Non-native English Speakers’ Text-Only Messages by Native and Non-native Speakers

Abstract : When people from different language backgrounds communicate, they need to adopt a common shared language, such as English, to set up the conversation. In conversations conducted over text-only computer-mediated communication (CMC) mediums, mutual exchange of socio-emotional information is limited to the use of words, symbols and emoticons. Previous research suggests that when message receivers share the same native language with the authors, they are more accurate at detecting the emotional valence of messages based on these cues compared to non-native speaking receivers. But is this still true when the messages are written by non-native speakers? Moreover, what message properties influence the accuracy of emotional valence detection? In this paper, we report on an experiment where native English speakers and Japanese non-native English speakers rate the emotional valence of text-only messages written by Japanese non-native English speaking authors. We analyze how three message properties, grammatical correctness, fluency of language and use of symbols and emoticons, influence emotional valence detection for native and non-native speakers. Based on our results, we propose theoretical and practical implications for supporting multilingual socio-emotional communication in text-only CMC.
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Ari Hautasaari, Naomi Yamashita. Emotion Detection in Non-native English Speakers’ Text-Only Messages by Native and Non-native Speakers. 15th Human-Computer Interaction (INTERACT), Sep 2015, Bamberg, Germany. pp.183-200, ⟨10.1007/978-3-319-22701-6_13⟩. ⟨hal-01599638⟩



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