Manual Sentiment Annotation for Punjabi News Headlines
OtherTextClassification
I scraped and manually annotated over 3,900 Punjabi news headlines to create a labeled dataset for training a sentiment classifier. The annotation process involved categorizing news headlines based on sentiment classes such as positive, negative, or neutral. The labeled data served as the foundation for fine-tuning a transformer-based language model for sentiment analysis in the Punjabi language. • Manually categorized text headlines for sentiment supervision
• Ensured consistent annotation guidelines and quality
• Collaborated with model training and evaluation tasks
• Directly enabled creation of a Punjabi sentiment AI classifier
I scraped and manually annotated over 3,900 Punjabi news headlines to create a labeled dataset for training a sentiment classifier. The annotation process involved categorizing news headlines based on sentiment classes such as positive, negative, or neutral. The labeled data served as the foundation for fine-tuning a transformer-based language model for sentiment analysis in the Punjabi language. • Manually categorized text headlines for sentiment supervision • Ensured consistent annotation guidelines and quality • Collaborated with model training and evaluation tasks • Directly enabled creation of a Punjabi sentiment AI classifier
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