Text Data

Text data analysis is a rapidly evolving field focused on extracting meaningful information and insights from textual sources. Current research emphasizes developing and refining methods for topic modeling, sentiment analysis, and causal inference using text, often leveraging large language models (LLMs) like BERT and GPT variants, along with other techniques such as graph-based word embeddings and transformer-based architectures. These advancements are crucial for improving various applications, including healthcare, finance, and social sciences, by enabling more accurate and efficient processing of the vast amounts of textual data generated daily. Furthermore, ongoing work addresses challenges like bias detection and mitigation in LLMs and the development of robust methods for handling code-mixed and noisy text data.