TEXT MINING: CLUSTERING USING BERT AND PROBABILISTIC TOPIC MODELING

Abstract

In order to find significant patterns and fresh ideas, free-form content is transformed into structured format using a process known as text mining or text data mining. It enables businesses to easily locate important information in texts like emails, social media posts, support requests, chatbots, and other sorts of text. Text mining enables businesses to anticipate possible threats from rivals, react quickly to production or delivery problems, and provide more individualised customer service. Businesses employ text mining for a range of functions, including production, IT, marketing, sales, and customer service. By carefully examining the phrases used in the source texts, topic modelling aims to pinpoint the recurrent themes in a corpus. These concepts are known as “topics”. As a result, textual data may be measured and used in quantitative analysis. In this sector, there are several subject modelling kinds that differ from one another based on a few unique traits and criteria. In our paper we have represented mainly 3 types of topic modelling techniques namely Latent Semantic Analysis (LSA), Hierarchical Dirichlet Process (HDP), and Latent Dirichlet Analysis (LDA) and calculated the coherence score of each method and compared them. And we have infused the concept of BERT with this topic modelling models and proposed a new model called HDP BERT and calculated the coherence Score and clusters the topics. At the end the n-grams features are applied to all 4 models and compared among each other in bases of uni, bi and trigram rate percentage.