Indonesia Exploring Popular X Thread Discussions on Trump Tariffs in Malaysia: A Bilingual and Profession-Based Sentiment Analysis with LIWC Model

Hairul Azhar Mohamad, Muhammad Haziq Abd Rashid, Amir Lukman Abd Rahman, Muhammad Luthfi Mohaini, Rasyiqah Batrisya Md Zolkapli, Sharifah Syazwa Amierah Syed Khalid, Arifuddin Abdullah, Urai SalamNon-Scopus1 Januari 2026Afiliasi: ID, MY

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This study examined sentiment in 600 highly viewed X threads discussing Trump’s tariffs affecting Malaysia from April 2 to August 10, 2025, comprising 300 English and 300 Malay threads (each 500–600 words; ≥10,000 views). Using LIWC, the study quantified positive and negative emotion-word percentages overall and by parts of speech (nouns, verbs, adjectives, adverbs) and compared sentiment patterns by user profession (professional vs non-professional). The results showed a slightly negative overall tone in both languages, with Malay threads marginally more negative than English threads. The sentiment concentrated most strongly in adjectives (followed by verbs), while nouns and adverbs contributed minimally, indicating that evaluative description carried much of the emotional loading in tariff discourse. Finally, profession influenced tone: professionals exhibited a more balanced sentiment profile than non-professionals, who expressed comparatively higher negative emotion levels. Future research should validate these patterns using real-time X data with transparent sampling logs and human annotation, strengthen profession identification through multi-signal verification, and extend comparisons across platforms (e.g., X versus TikTok) to test whether platform affordances shape bilingual framing over time.

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