Topic modeling aims to discover hidden topics or themes across documents that capture semantic information beyond individual words. It aims to address a key challenge in building a machine learning algorithm that learns from text data by going beyond the lexical level of what has been written to the semantic level of what was intended. The resulting topics can be used to annotate documents based on their association with various topics.
In other words, topic modeling aims to automatically summarize large collections of documents to facilitate organization and management, as well as search and recommendations. At the same time, it can enable the understanding of documents to the extent that humans can interpret the descriptions of topics.
Topic models aim to address the curse of dimensionality that can plague the bag-of-words model....
