Public Perception of Chinese Language Education in Saudi Arabia: A Keyword-Enhanced Aspect-Based Sentiment Analysis of Social Media Discourse
DOI:
https://doi.org/10.21015/vtse.v13i4.2307Abstract
The increasing popularity of Chinese as a second language in Saudi Arabia offers a unique chance to study and explore public perceptions and opinions through computational methods. Natural Language Processing (NLP) is one such method, widely adopted in research for this type of analysis. It offers techniques to extract insights from large volumes of unstructured data. However, sentiment analysis on multilingual, culturally specific online discourse remains a challenging task in NLP. The aim of this research work is to address the problem of accurately detecting sentiments and topics in contexts influenced by culture; we discuss the adoption of Chinese language education in Saudi Arabia. For this, we implemented a transformer-based sentiment analysis model on a custom domain-specific dataset with LDA for topic modeling. In this way, we identified key thematic clusters related to globalization, education, and cultural exchange. The research results indicate that topics associated with globalization carry the most positive sentiment, reflecting optimistic public attitudes toward linguistic expansion. This work contributes to the field of applied NLP by demonstrating the feasibility of sentiment and topic modeling in low-resource, culturally diverse environments and contexts.
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