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license: mit |
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task_categories: |
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- text-classification |
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language: |
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- uk |
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- ru |
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tags: |
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- sentiment |
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- social |
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- networks |
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- Telegram |
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size_categories: |
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- 10K<n<100K |
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--- |
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The corpus was constructed from multiple sources to ensure diversity and representation of real-world Ukrainian social discourse. We systematically scraped comments and posts from Ukrainian Telegram channels, collecting content dated between February 2022 and September 2024. |
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The volume of the scraped documents amounts to 8,064 texts. Also, we integrated two publicly available datasets: TG samples from D. Baida (https://huggingface.co/datasets/dmytrobaida/autotrain-data-ukrainian-telegram-sentiment-analysis) with 3,000 samples and 1,000 Yakaboo book reviews (https://github.com/osyvokon/awesome-ukrainian-nlp). |
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Furthermore, 1,000 product reviews from Hotline.ua were incorporated to diversify the content domains. |
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After deleting duplicates and boilerplate content, the final corpus included 12,224 texts covering various topics including politics, governmental services, entertainment, daily life, and consumer reviews. |
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All texts in languages other than Ukrainian and Russian were filtered out due to their statistical insignificance. |
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Five annotators participated in the project, are native Ukrainian speakers with bilingual proficiency in Russian. The annotation guidelines instructed participants to classify texts according to four sentiment categories: |
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* Positive: posts containing expressions that reflect positive emotions (joy, support, admiration etc). |
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* Negative: texts containing expressions that reflect negative emotions (criticism, sarcasm, condemnation, aggression, doubt, fear etc). |
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* Neutral: documents where the author does not use either positive or negative expressions. |
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* Mixed: texts containing expressions from both positive and negative emotional spectra. |
