Text Mining

Text mining leverages computational techniques to extract meaningful information from unstructured text data, aiming to automate knowledge discovery and analysis across diverse fields. Current research emphasizes the application of deep learning models, such as BERT and its variants, along with other machine learning classifiers like logistic regression, to tasks including named entity recognition, sentiment analysis, and topic modeling, often incorporating ontologies for enhanced knowledge representation. This rapidly evolving field significantly impacts various sectors, enabling improved decision-making in areas like healthcare (e.g., literature-based drug discovery), finance (e.g., risk detection), and social sciences (e.g., analyzing public opinion on policy issues). The development of efficient and accessible text mining tools is also a key focus.