Topic Modeling is an unsupervised learning technique used in machine learning that aims to uncover the hidden structure of large sets of textual data. It is a type of statistical modeling used to discover the abstract topics hidden within a collection of documents. It is used to summarize the contents of a document more effectively than conventional keyword extraction, by clustering the document’s content into topics.
Topic Modeling is used for a variety of applications, such as text summarization, identifying trends in large sets of textual data, and topic identification. It can be used to improve the accuracy of predictive models and to understand the structure of the data.
The main purpose of Topic Modeling is to divide the documents into “topics”, which represent clusters of words that often appear together in the documents. The topics are then represented as distributions of words, called topic vectors, whose probabilities define the probability of words belonging to a particular topic.
In most cases, the number of topics must be specified before the Topic Modeling process can begin. The software then assigns topics to each of the documents, based on the topic probabilities assigned to each word.
One of the most popular algorithms used for Topic Modeling is Latent Dirichlet Allocation (LDA) which is a generative model that learns classification structure from large collections of documents. It has become increasingly popular for topic modelling due to its flexibility and effectiveness in representing multiple topics.
Topic modeling is a powerful tool for data scientists, as it allows them to analyze large amounts of unstructured data, discover hidden patterns, and generate interpretable results. It also reduces the amount of time spent in understanding and manually summarizing text, making it easier to interpret graphical representations of data. In addition, Topic Modeling can be applied to multiple types of data such as audio, images, and even videos.
