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

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.

Sentiment analysis; Twitter/X threads; Trump tariffs; Malaysia

1. Ali, R. H., Pinto, G., Lawrie, E., & Linstead, E. J. (2022). A large-scale sentiment analysis of tweets pertaining to the 2020 US presidential election. Journal of Big Data, 9(1), 79. https://doi.org/10.1186/s40537-022-00633-z [Google Scholar] [Crossref]

2. Amiti, M., Redding, S. J., & Weinstein, D. E. (2019). The impact of the 2018 tariffs on prices and welfare. Journal of Economic Perspectives, 33(4), 187–210. https://doi.org/10.1257/jep.33.4.187 [Google Scholar] [Crossref]

3. Barreto, S., Moura, R., Carvalho, J., Paes, A., & Plastino, A. (2023). Sentiment analysis in tweets: An assessment study from classical to modern word representation models. Data Mining and Knowledge Discovery, 37(1), 318–380. https://doi.org/10.1007/s10618-022-00853-0 [Google Scholar] [Crossref]

4. Boon-Itt, S., & Skunkan, Y. (2020). Public perception of the COVID-19 pandemic on Twitter: Sentiment analysis and topic modeling study. JMIR Public Health and Surveillance, 6(4), e21978. https://doi.org/10.2196/21978 [Google Scholar] [Crossref]

5. Caetano, J. A., Lima, H. S., Santos, M. F., & Marques-Neto, H. T. (2018). Using sentiment analysis to define Twitter political users’ classes and their homophily during the 2016 American presidential election. Journal of Internet Services and Applications, 9, 18. https://doi.org/10.1186/s13174-018-0089-0 [Google Scholar] [Crossref]

6. Chandrasekaran, G., Nguyen, T. N., & Duraisamy, H. J. (2021). Multimodal sentimental analysis for social media applications: A comprehensive review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(5), e1415. https://doi.org/10.1002/widm.1415 [Google Scholar] [Crossref]

7. Fetzer, T., & Schwarz, C. (2021). Tariffs and politics: Evidence from Trump’s trade wars. The Economic Journal, 131(636), 1717–1741. https://doi.org/10.1093/ej/ueaa122 [Google Scholar] [Crossref]

8. Gayo-Avello, D. (2013). A meta-analysis of state-of-the-art electoral prediction from Twitter data. Social Science Computer Review, 31(6), 649–679. https://doi.org/10.1177/0894439313493979 [Google Scholar] [Crossref]

9. Kübler, R. V., Colicev, A., & Pauwels, K. H. (2020). Social media’s impact on the consumer mindset: When to use which sentiment extraction tool? Journal of Interactive Marketing, 50, 136–155. https://doi.org/10.1016/j.intmar.2019.08.001 [Google Scholar] [Crossref]

10. Liu, B. (2012). Sentiment analysis and opinion mining. Morgan & Claypool Publishers. https://doi.org/10.2200/S00416ED1V01Y201204HLT016 [Google Scholar] [Crossref]

11. Maskat, R., Azman, N. A., Nulizairos, N. S. S., Zahidin, N. A., Mahadi, A. H., Norshamsul, S. R., & Mahdin, H. (2024). A bi-annotated Malay-English code-switching (Manglish) dataset of X posts for biological gender identification and authorship attribution. Data in Brief, 52, 110034. https://doi.org/10.1016/j.dib.2024.110034 [Google Scholar] [Crossref]

12. Md Zolkapli, R. B., Mohd Kenali, S. F., Abdul Hadi, N. F., Basiron, M. K., Shaharudin, N. A. D., & Mohamad, H. A. (2024). Exploring reasons for learning English and burnout among pre-university students. Malaysian Journal of Social Sciences and Humanities (MJSSH), 9(1), e002670. https://doi.org/10.47405/mjssh.v9i1.2670 [Google Scholar] [Crossref]

13. Md Zolkapli, R. B., Mohd Kenali, S. F., Abdul Hadi, N. F., Jaafar, A. J., Mohamad, H. A., Abd Rahman, A. L., & Shaharudin, N. A. D. (2025). Addressing English grammar learning challenges among Malaysian Islamic studies students. Malaysian Journal of Social Sciences and Humanities (MJSSH), 10(1), e003199. https://doi.org/10.47405/mjssh.v10i1.3199 [Google Scholar] [Crossref]

14. Milli, S., Carroll, M., Wang, Y., Pandey, S., Zhao, S., & Dragan, A. D. (2025). Engagement, user satisfaction, and the amplification of divisive content on social media. PNAS Nexus, 4(3), pgaf062. https://doi.org/10.1093/pnasnexus/pgaf062 [Google Scholar] [Crossref]

15. Mohamad, H. A., Md Zolkapli, R. B., Abdul Wahab, N. H., Mohaini, M. L., Ravinthra Nath, P., & Abd Rashid, M. H. (2024). The persuasiveness of research abstracts through analytical thinking and emotional tone domains in academic research writing. In M. F. Ubaidillah et al. (Eds.), Proceedings of the 4th International Conference on English Language Teaching (ICON-ELT 2023) (Advances in Social Science, Education and Humanities Research, Vol. 780). Atlantis Press. https://doi.org/10.2991/978-2-38476-120-3_9 [Google Scholar] [Crossref]

16. Mohamad, H. A., Pilus, Z., Md Zolkapli, R. B., Mohaini, M. L., Abdul Wahab, N. H., & Nath, P. R. (2023). The rhetorical density of authorial emotiveness and voice passiveness in abstract compositions. International Journal of Asian Social Science, 13(2), 61–77. https://doi.org/10.55493/5007.v13i2.4726 [Google Scholar] [Crossref]

17. Mohamad, H. A., Pilus, Z., Mohaini, M. L., Md Zolkapli, R. B., & Abdul Wahab, N. H. (2022). Rhetorical strategies for appeal to logos in research abstracts: An analysis of rhetoric. Malaysian Journal of Social Sciences and Humanities (MJSSH), 6(3), 1–15. https://doi.org/10.33306/mjssh/200 [Google Scholar] [Crossref]

18. Nip, J. Y. M., & Berthelier, B. (2024). Social media sentiment analysis. Encyclopedia, 4(4), 1590–1598. https://doi.org/10.3390/encyclopedia4040104 [Google Scholar] [Crossref]

19. Onn, N. A. Y., Md Zolkapli, R. B., Mohd Kenali, S. F., Abd Rahman, A. L., Abdul Hadi, N. F., & Mohamad, H. A. (2024). Exploring the potential of TikTok as a supplementary tool for English language learning among students. Malaysian Journal of Social Sciences and Humanities (MJSSH), 8(2), 103–114. https://doi.org/10.33306/mjssh/279 [Google Scholar] [Crossref]

20. Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015. The University of Texas at Austin. https://doi.org/10.15781/T29G6Z [Google Scholar] [Crossref]

21. Qi, Y., & Shabrina, Z. (2023). Sentiment analysis using Twitter data: A comparative application of lexicon- and machine-learning-based approach. Social Network Analysis and Mining, 13(1), 31. https://doi.org/10.1007/s13278-023-01030-x [Google Scholar] [Crossref]

22. Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24–54. https://doi.org/10.1177/0261927X09351676 [Google Scholar] [Crossref]

23. Wang, Y., Guo, J., Yuan, C., & Li, B. (2022). Sentiment analysis of Twitter data. Applied Sciences, 12(22), 11775. https://doi.org/10.3390/app122211775 [Google Scholar] [Crossref]

24. Yadollahi, A., Shahraki, A. G., & Zaiane, O. R. (2017). Current state of text sentiment analysis from opinion to emotion mining. ACM Computing Surveys, 50(2), Article 25. https://doi.org/10.1145/3057270 [Google Scholar] [Crossref]

25. Zainab, K., Srivastava, G., & Mago, V. (2022). Identifying health related occupations of Twitter users through word embedding and deep neural networks. BMC Bioinformatics, 22(Suppl 10), 630. https://doi.org/10.1186/s12859-022-04933-2 [Google Scholar] [Crossref]

26. Zimbra, D., Abbasi, A., Zeng, D., & Chen, H. (2018). The state-of-the-art in Twitter sentiment analysis: A review and benchmark evaluation. ACM Transactions on Management Information Systems, 9(2), Article 5. https://doi.org/10.1145/3185045 [Google Scholar] [Crossref]

• Sin Reconsidered: A Comparative Study of Classical Theology and Murphy-O’Connor’s Pauline Anthropology
• Spiritual Warfare as Intervention: An Ethnographic Analysis of Johane Masowe Chishanu Church in Combating Drug Abuse in Zimbabwe
• Conceptualizing Muslim and Non-Muslim Social Interaction in Malaysia Based on the Maqasid Shariah Framework
• Melchizedek and the Levitical Priesthood: A Comparative Theological Study
• Yoruba Concept of Omoluabi: An Ideaological Antidote against Corruption in Nigeria