Topic Modelling

Topic modeling is a natural language processing technique used to discover underlying themes and patterns within large collections of text data, aiming to automatically organize and summarize information. Current research emphasizes improving topic coherence and interpretability, often leveraging advanced embedding techniques from transformer-based language models and incorporating clustering algorithms like K-means and density-based methods within architectures such as BERTopic. This field is significant for its applications in various domains, including sentiment analysis, fake news detection, and healthcare, enabling researchers to extract meaningful insights from massive textual datasets and facilitating more effective data-driven decision-making.