2026· International journal of research and scientific innovation· Vol 13, pp. 4785-4790· 0 citations
TL;DR
A comprehensive set of observations regarding sentiment analysis of Indian language code-mixed social media text is provided and it is suggested that the transformer-based models trained on Indian language corpora outperform others.
Abstract
The exponential rise of social media has led to the generation of a large amount of informal text. Especially, code-mixed languages have gained substantial popularity among social media users. In India, the code-mixed language Kannada-English is widely used in social media platforms. This informal and non-standard language form brings forward significant challenges to natural language processing (NLP) tasks like sentiment analysis. This paper presents a detailed analysis of sentiment analysis in kannada-english code-mixed social media text. A manually annotated dataset is created and classified into positive, negative, and neutral sentiment labels. Various kinds of machine learning, deep learning, and transformer-based models are evaluated. The results suggest that the transformer-based models trained on Indian language corpora outperform others. Furthermore, this paper provides a comprehensive set of observations regarding sentiment analysis of Indian language code-mixed social media text.
Overall, this work demonstrates that incorporating explicit linguistic information, including language identity, sentiment polarity, and intensifier information, improves sentiment classification of Gujarati–English code-mixed text.
Chirag D. Shah, Shailesh A. Chaudhari· International journal of com...· 0 citations
Abstract People across India increasingly use regional languages online to share opinions, reviews, and reactions, but understanding the sentiment behind this text is not easy. Indian regional languages are often used in informal ways, mixed with English, written in different scripts, and supported by only a small numb...
R. V.· International Journal of Tec...· 0 citations
In the context of the digital economy, e-commerce and social media have generated massive amounts of short Chinese web texts, making the accurate extraction of sentiment information a critical requirement for market analysis and public opinion monitoring. Short texts are characterized by fragmented information expressi...
Yingying Cai, Jinliang Ma· International Conference on...· 0 citations
Hate speech and value-violating content on Indonesian social media, compounded by code-mixed language, threaten social cohesion. This study proposes a Pancasila-based Aspect Category Sentiment Analysis framework grounded in Indonesia’s five foundational values: Divinity, Humanity, Unity, Democracy, and Social Justice....
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Findings indicate that explicit domain-specific lexical knowledge can support an interpretable sentiment representation while making the effects of lexical coverage and topic-routing uncertainty visible.
Kyriakos Skoularikis, I. Savvas· Applied Sciences· 0 citations
Examination of sentiment analysis methods applied to social media text data, covering lexicon-based, machine learning, and deep learning approaches, including transformerbased architectures, as well as widely used datasets, shows how sentiment analysis can be applied to the detection of threats that exploit human emoti...
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