Sentiment Data Labeler for ML Model Training
TextClassification
I manually labeled over 2,000 product reviews with sentiment categories of Positive, Negative, or Neutral for a sentiment analysis project. Consistency was maintained by carefully assessing edge cases, and mislabeled examples were corrected after model validation. This process directly contributed to improving model training accuracy and performance.
• Labeled textual product reviews with sentiment classification.
• Ensured labeling consistency by reviewing borderline sentiment cases.
• Cleaned and preprocessed review text before and after annotation.
• Performed quality assurance by validating labels against model outputs.
I manually labeled over 2,000 product reviews with sentiment categories of Positive, Negative, or Neutral for a sentiment analysis project. Consistency was maintained by carefully assessing edge cases, and mislabeled examples were corrected after model validation. This process directly contributed to improving model training accuracy and performance. • Labeled textual product reviews with sentiment classification. • Ensured labeling consistency by reviewing borderline sentiment cases. • Cleaned and preprocessed review text before and after annotation. • Performed quality assurance by validating labels against model outputs.
2026 - 2026