Survey on Text Mining using Bert and Probabilistic Topic Modelling

The method transforming free-form content into structured format with the purpose of identifying significant patterns and fresh insights is often referred to as text mining and text data mining. Companies are able to investigate and find by utilising cutting-edge analytical techniques like Naive Bayes, Support Vector Machines (SVM), and other deep learning algorithms, they may uncover hidden correlations within their unstructured data. Using text Mining tools, for instance, enables you to quickly identify unfavorable sentiment from different social media posts and comments so we may take action and address the issue. A text Mining technique called keyword extraction mechanically extracts the most important and frequently used words and expressions from a document. It assists in identifying the main points made in the text and in summarizing it. It lets companies quickly find valuable information in texts such as emails, social media postings, support tickets, chatbots, and other types of text. Businesses can use text mining to foresee potential threats from competitors, respond promptly to production or delivery issues, and offer more individualized customer care. Text mining is used by businesses for a variety of purposes, including production, IT, marketing, sales, and customer support. In this paper, we have discussed Text Mining and how Probabilistic Topic Modelling and BERT are related to Text Mining.