Data Annotator
In this project, I worked as a Data Annotator on a text classification dataset designed to train and improve a natural language processing (NLP) model. The dataset consisted of short text entries, including customer feedback, product reviews, and general user-generated content. My primary responsibility was to accurately classify each text sample into predefined categories such as positive, negative, neutral sentiment, or topic-based classes according to detailed annotation guidelines. I carefully reviewed each text entry to understand context, tone, and intent before assigning the appropriate label. I handled ambiguous cases by applying guideline rules consistently and flagging uncertain samples for review when necessary. In addition to labeling, I performed quality checks to ensure consistency and accuracy across batches. My contributions helped improve the quality and reliability of the training dataset, supporting the development of a more accurate and robust AI model.