I worked on a text annotation project where I reviewed and labeled written content for sentiment and intent classificati
I worked on a text annotation project where I reviewed and labeled written content for sentiment and intent classification. The task involved reading customer comments and categorizing them as positive, negative, or neutral, while also identifying the primary topic being discussed. I followed detailed annotation guidelines to ensure consistency and accuracy across the dataset. I regularly reviewed edge cases and verified labels before submission. I used spreadsheet tools such as Microsoft Excel and Google Sheets for data organization and annotation tracking, and I became familiar with annotation workflows commonly used in AI training projects. Through this work, I developed strong attention to detail, the ability to follow complex instructions, and an understanding of how labeled data contributes to machine learning model performance. I have worked on AI evaluation platforms such as DataAnnotation and Outlier: I evaluated AI-generated responses by assessing their accuracy, relevance, clarity, and adherence to instructions. I compared multiple responses, identified factual errors, and provided ratings and feedback to improve model performance, which required critical thinking, an d esearch skills, which align with project guidelines to maintain high-quality evaluation standards.