Data Annotator (Punjabi News Sentiment Classifier)
TextClassification
This project involved manually annotating over 3,900 Punjabi news headlines to generate high-quality labeled data for a low-resource language. I created the ground truth labels for sentiment classification, preparing the dataset for fine-tuning a BERT-based language model. The process required careful reading, understanding, and categorizing of headlines based on their sentiment (positive, negative, neutral).• Manual annotation of news headlines for sentiment analysis
• Developed a custom dataset for Punjabi language processing
• Prepared data for fine-tuning L3Cube-Punjabi-BERT model
• Ensured dataset quality and consistency through diligent review.
This project involved manually annotating over 3,900 Punjabi news headlines to generate high-quality labeled data for a low-resource language. I created the ground truth labels for sentiment classification, preparing the dataset for fine-tuning a BERT-based language model. The process required careful reading, understanding, and categorizing of headlines based on their sentiment (positive, negative, neutral).• Manual annotation of news headlines for sentiment analysis • Developed a custom dataset for Punjabi language processing • Prepared data for fine-tuning L3Cube-Punjabi-BERT model • Ensured dataset quality and consistency through diligent review.
2023 - 2023