Survey Response

Survey response analysis is a rapidly evolving field focused on efficiently extracting meaningful insights from diverse data sources, including both structured (e.g., multiple-choice) and unstructured (e.g., open-ended text) responses. Current research emphasizes leveraging machine learning techniques, such as natural language processing (NLP) with models like BERT and other transformer architectures, and aspect-based sentiment analysis, to automate the processing and interpretation of large-scale survey data. This work is crucial for improving the efficiency and accuracy of social science research, human resource management, and other fields that rely heavily on survey data for decision-making, while also highlighting potential biases inherent in both human and AI-generated responses.