A Multi-Level Hybrid Architecture for Structured Sentiment Analysis
Zulkhazhav A. Bekmanova G. Yergesh B. Nazyrova A. Lamasheva Z. Aimicheva G.
January 2026Multidisciplinary Digital Publishing Institute (MDPI)
Electronics (Switzerland)
2026#15Issue 2
This paper presents a hybrid architecture for automatic sentiment analysis of Kazakh-language political discourse. The Kazakh language is characterized by an agglutinative structure, a complex word-formation system, and the limited availability of digital resources, which significantly complicates the application of standard neural network approaches. To account for these characteristics, a multi-level system was developed that combines morphological and syntactic analysis rules, ontological relationships between political concepts, and multilingual representations of the XLM-R model, used in zero-shot mode. A corpus of 12,000 sentences was annotated for sentiment polarity and used for training and evaluation, while Universal Dependencies annotation was applied for morpho-syntactic analysis. Rule-based components compensate for errors related to affixation variability, modality, and directive constructions. An ontology comprising over 300 domain concepts ensures the correct interpretation of set expressions, terms, and political actors. Experimental results show that the proposed hybrid architecture outperforms both neural network baseline models and purely rule-based solutions, achieving Macro-F1 = 0.81. Ablation revealed that the contribution of modules is unevenly distributed: the ontology provides +0.04 to Macro-F1, the UD syntax +0.08, and the rule-based module +0.11. The developed system forms an interpretable and robust assessment of tonality, emotions, and discursive strategies in political discourse, and also creates a basis for further expansion of the corpus, additional training of models, and the application of hybrid methods to other tasks of analyzing low-resource languages.
agglutinative languages , hybrid architecture , Kazakh language , modal constructions , ontology , political discourse , sentiment analysis , Universal Dependencies , XLM-R
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Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, 2 Satpayev Str., Astana, 010008, Kazakhstan
Faculty of Information Technologies
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