AI Training Data Annotation for Customer Interaction Analytics
This project involved preparing and labeling large volumes of customer interaction data to support the training of conversational AI and customer analytics models. I worked with structured and unstructured datasets consisting of chat transcripts, support tickets, and customer feedback records. My primary responsibilities included annotating text data for intent classification, sentiment analysis, and entity recognition to improve the accuracy of natural language processing (NLP) models. The work required consistent tagging of customer intents (e.g., billing inquiry, technical issue, account update), identifying named entities such as product names, transaction references, and service categories, and labeling sentiment indicators to train supervised learning models. The dataset consisted of thousands of interaction records, and strict annotation guidelines were followed to ensure consistency and reliability across the dataset. I also performed dataset validation, quality checks, and annotation reviews to maintain high accuracy and reduce labeling errors.