Update README.md

README.md

@@ -18,9 +18,11 @@ The corpus was constructed from multiple sources to ensure diversity and represe
 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). 
 Furthermore, 1,000 product reviews from Hotline.ua were incorporated to diversify the content domains.
 
-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.
+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. 
+All texts in languages other than Ukrainian and Russian were filtered out due to their statistical insignificance.
 
-The annotation guidelines instructed participants to classify texts according to four sentiment categories:
+
+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:
 * Positive: posts containing expressions that reflect positive emotions (joy, support, admiration etc).
 * Negative: texts containing expressions that reflect negative emotions (criticism, sarcasm, condemnation, aggression, doubt, fear etc).
 * Neutral: documents where the author does not use either positive or negative expressions.