Exploring Cultural Nuances in Emotion Perception Across 15 African Languages

• URL: http://arxiv.org/abs/2503.19642v1
• Date: Tue, 25 Mar 2025 13:30:03 GMT
• Title: Exploring Cultural Nuances in Emotion Perception Across 15 African Languages
• Authors: Ibrahim Said Ahmad, Shiran Dudy, Tadesse Destaw Belay, Idris Abdulmumin, Seid Muhie Yimam, Shamsuddeen Hassan Muhammad, Kenneth Church,
• Abstract summary: Cross-linguistic analysis of emotion expression in 15 African languages.<n>We examine four key dimensions of emotion representation: text length, sentiment polarity, emotion co-occurrence, and intensity variations.<n>We observe a higher prevalence of negative sentiment in several Nigerian languages compared to lower negativity in languages like IsiXhosa.
• Score: 8.894537613998516
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Understanding how emotions are expressed across languages is vital for building culturally-aware and inclusive NLP systems. However, emotion expression in African languages is understudied, limiting the development of effective emotion detection tools in these languages. In this work, we present a cross-linguistic analysis of emotion expression in 15 African languages. We examine four key dimensions of emotion representation: text length, sentiment polarity, emotion co-occurrence, and intensity variations. Our findings reveal diverse language-specific patterns in emotional expression -- with Somali texts typically longer, while others like IsiZulu and Algerian Arabic show more concise emotional expression. We observe a higher prevalence of negative sentiment in several Nigerian languages compared to lower negativity in languages like IsiXhosa. Further, emotion co-occurrence analysis demonstrates strong cross-linguistic associations between specific emotion pairs (anger-disgust, sadness-fear), suggesting universal psychological connections. Intensity distributions show multimodal patterns with significant variations between language families; Bantu languages display similar yet distinct profiles, while Afroasiatic languages and Nigerian Pidgin demonstrate wider intensity ranges. These findings highlight the need for language-specific approaches to emotion detection while identifying opportunities for transfer learning across related languages.

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