Textual emotion detection has gained unprecedented interest over the years due to its wide practical applications, but existing emotion corpora are skewed toward English. Malay, a resource-poor language, currently does not have a reliable emotion corpus that can be used as a benchmark, particularly in the variation of the Malay language used in Malaysia. We present EmoTweet-Malay-6, the first carefully curated Malay emotion corpus containing 5526 tweets annotated with six emotion categories: anger, fear, happiness, love, sadness, surprise and none (no emotion). Since Malaysians are typically at least bilingual, a fraction of this corpus is also code-switched (English and Malay). The tweets were initially annotated using a rule-based classifier following the original source, but we enhanced the corpus by providing additional human validation. Three native Malay speakers were trained to annotate these tweets and achieved Fleiss’ kappa of 0.604. We benchmarked EmoTweet-Malay-6 using commonly adopted machine learning, deep learning, and pre-trained language models. The Malay emotion classifiers serving as baselines show good performance with a macro [Formula: see text]1-score ranging between 0.81 to 0.95 across all six emotions. EmoTweet-Malay-6 serves as an essential resource to advance the development and study of emotion detection in the Malay language.
In this paper, we introduce ViFinClass, the first large-scale benchmark dataset for Vietnamese financial news topic classification. Collected from CafeF between 2006 and 2023, the dataset contains over 30,000 curated articles across five major financial topics: Banking and Finance, Stock Market, Real Estate, International Finance, and Macroeconomics. We evaluate multiple baselines, including traditional machine learning models and transformer-based approaches. Experimental results show that PhoBERT achieves the best performance, outperforming multilingual models such as XLM-R. These findings highlight the importance of domain-specific resources in improving financial NLP for low-resource languages. ViFinClass provides a solid foundation for future research on text classification, information extraction, and financial sentiment analysis in Vietnamese, supporting both academic studies and real-world financial applications.
This study compares human and ChatGPT 4o processing of ox (“niu”, 牛) and dragon (“long”, 龍) metaphors in English and Chinese, exploring how cultural values and AI architecture influence interpretation. We used 30 corpus-derived sentences from Academia Sinica Balanced Corpus of Modern Chinese (ASBC) (Chinese Knowledge and Information Processing, 2010) and Corpus of Contemporary American English (COCA) (Davies, 2008), rated by 15 Chinese speakers, 15 English speakers, and ChatGPT 4o on a 5-point Likert scale. A key methodological step involved post-hoc categorization of sentences into daily, literary, and formal context types, which revealed significant quantitative divergence. Findings show statistically significant differences. Human participants demonstrated more context-sensitive and stable understanding; ChatGPT 4o showed high volatility (CV) with complex idioms like “having a cow.” For dragon metaphors, ChatGPT 4o systematically amplified cultural contrasts, leading to a strong cultural polarization effect, particularly in formal contexts. This suggests that while AI is a powerful linguistic tool, its statistical modeling architecture results in a statistically biased representation of cultural knowledge, lacking the nuance and stability of human cognition.
This study provides substantial evidence on the reading behavior of native Mizo speakers’ L1 and L2, which are Mizo and English, respectively. It primarily focuses on reading ambiguous sentences containing homographs. Two experiments observed the reaction time (RT) of Mizo native speakers reading text in L1 and L2, respectively. A comparative analysis of the RT of both L1 and L2 was done to obtain deeper learning about the cognitive processes involved while reading the two languages. Ambiguous sentences were read in significantly longer times in both L1 and L2. Besides, findings reveal that the readers read ambiguous sentences in L1 significantly longer than in L2. This suggests that Mizo speakers can read and process their L2 quicker than their L1 in specific sentences containing homographs. The reason behind this is the absence of tone information of the Mizo homographs, which lengthens reading time and requires the extraction of their meanings and pronunciations.
Language has a profound impact on communication and thought for human beings. It has been instrumental in the dissemination of disaster warning and rescue information during emergencies. Due to the decline in language abilities and the physiological and social disadvantages of not keeping pace with the speed of information technology development, elderly individuals have become a vulnerable group on the linguistic level. Based on field surveys, this paper focuses on the elderly population in rural China and discusses the language barriers faced by this group in emergencies and the emergency language services they received. The research found: (1) The rural elderly’s individual language abilities are so poor that they are less able to cope with emergencies. (2) There is a pronounced barrier for the rural elderly in terms of communication and information acquisition. (3) Current rural emergency language services face issues such as limited service content, lack of professional service teams, insufficient service awareness, and an incomplete service mechanism. Based on these issues, rural emergency language services should provide differentiated services according to the linguistic characteristics of the elderly population and the actual conditions of rural communities. This involves forming specialized, localized emergency language service teams, extending the breadth and depth of emergency language services, and establishing a long-term mechanism to better protect the linguistic rights and emergency needs of the elderly.
