# Vegetarianism Discourse in Russian Social Media: A Case Study
## 8 Applications and Limitations  
The task described in this paper and its results have potential applications for monitoring public opinion on dietary choices, enabling a more precise analysis of social attitudes toward vegetarianism and related lifestyle trends. Classification model insights can help policymakers and health organizations to identify public concerns and create targeted campaigns to address common misconceptions or promote dietary shifts. Additionally, businesses in the food industry can use these models to analyze consumer sentiment and adjust marketing strategies to align with pro- or anti-vegetarian perspectives.  
Our results indicate that while VegRuCorpus provides a valuable resource for opinion mining in Russian, the classification accuracy achieved by both traditional and transformer-based models highlights challenges in effectively capturing diverse opinions on vegetarianism. Additionally, the complexity of the Russian language, including its rich morphology and flexible syntax, further complicates accurate classification.  
A key limitation of this study is its reliance on the relatively small dataset, VegRuCorpus, which may not fully reflect the diversity of opinions on vegetarianism across various demographics or regions. Moreover, the models, particularly the fine-tuned transformers, perform poorly, indicating a need for more extensive task-specific training data to improve accuracy. The lack of improvement using topic modeling and sentiment analysis vectors suggests that these features are less effective for the task, emphasizing the need for more sophisticated text representations. Finally, although contrastive learning shows promise, its use is constrained by the computational demands of large transformer models like ruRoBERTa, which limits scalability and generalizability in practical applications.  
To address these limitations, future work could focus on expanding VegRuCorpus with more annotated examples, encompassing diverse contexts and nuanced opinions. Incorporating additional linguistic features, such as contextual embeddings from multilingual or Russian-specific transformers, could also improve model performance. Also, the application of advanced techniques like domain-adaptive pretraining and data augmentation may help models to better generalize to this challenging classification task.  
**Author Contributions:** Methodology, N.V.; Software, N.G. and N.V.; Validation, N.G. and N.V.; Formal analysis, N.V.; Investigation, N.G.; Data curation, N.G. and N.V.; Writing--original draft, N.G. and N.V.; Writing--review & editing, N.V.; Visualization, N.G.; Supervision, N.V.; Project administration, N.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset is stored at GitHub repository [[https://github.com/[PERSON]/VegRuCorpus](https://github.com/[PERSON]/VegRuCorpus)]([https://github.com/[PERSON]/VegRuCorpus](https://github.com/[PERSON]/VegRuCorpus)). It is publicly accessible for research purposes.  
**Conflicts of Interest:** The authors declare no conflict of interest.