Recently, computational sentiment analysis has become an essential tool for detecting evaluative language in large text collections. However, its application to many low-resource language families and historical corpora remains largely unexplored. This paper reviews the evolution of sentiment analysis methods in the Turkic language family, with a particular focus on Chagatai, the classical predecessor of several modern Turkic languages. We outline the methods that have evolved since the advent of lexicon-based and rule-based approaches up to the present day with large language models, addressing longstanding problems in agglutinative morphology, data scarcity, orthographic instability, and multilingual lexical mixing. To examine the available options, we conducted a pilot experiment using multilingual models in a zero-shot setting on a curated Chagatai corpus. In the absence of ground-truth annotations, prediction stability was validated with ensemble consistency and inter-model agreement. The results show real promise but also distinct limitations when adapting traditional NLP technologies for historically remote, low-resource languages. Progress in the field will require cross-disciplinary work, systematic diachronic dataset deployment, and a nuanced adaptation of multilingual representation learning to handle linguistically rich, low-resource settings.
Baishemirov et al. (Thu,) studied this question.