Customer Review Sentiment Labeling
TextClassification
I developed a text annotation project focused on labeling customers reviews in the food and restaurant domain.
The goal was to classify each review into positive, negative or nuetral sentiment.
I created clear annotation guidlines to ensure consistency, including how to handle ambiguous or mixed opinions. i labeled a dataset of over 50 - 100 reviews and included confidence scores for quality control.
I performed a review pass to identify and correct inconsistencies, ensuring high-quality labeled data suitable for training machine leaning models.
I developed a text annotation project focused on labeling customers reviews in the food and restaurant domain. The goal was to classify each review into positive, negative or nuetral sentiment. I created clear annotation guidlines to ensure consistency, including how to handle ambiguous or mixed opinions. i labeled a dataset of over 50 - 100 reviews and included confidence scores for quality control. I performed a review pass to identify and correct inconsistencies, ensuring high-quality labeled data suitable for training machine leaning models.
2025 - 2025