Text Analysis
Text analysis focuses on extracting meaningful information and insights from textual data using computational methods. Current research heavily utilizes large language models (LLMs), such as BERT and GPT variants, along with deep learning architectures like CNNs and RNNs, to perform tasks ranging from sentiment analysis and topic modeling to more complex applications like predicting stock movements and identifying rhetorical roles in legal documents. These advancements are significantly impacting various fields, enabling more efficient and scalable analysis of large text corpora in areas such as social sciences, political science, healthcare, and finance.
Papers
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