Rapid Text Retrieval and Analysis Supporting Latent Dirichlet Allocation Based on Probabilistic Models

Abstract

Text mining, also known as text analysis, is the process of converting unstructured text data into meaningful and functional information. Text mining uses different AI technologies to automate data and generate valuable insights, allowing enterprises to make data-based decisions. Text mining enables the user to extract important content from text data sets. Text analysis encourages machine learning ability for research areas such as medical and pharmaceutical innovation fields. Apart from this, text analysis converts inaccessible data into a structured format, which can be used for further analysis. Text analysis emphasizes facts and relationships from large data sets. This information is extracted and converted into structured data for visualization, analysis, and integration as structured data and refines the information using machine-learning methods. Like most things related to Natural Language Processing, text mining can seem like a difficult concept to understand. But the fact is, it does not have to be. This research article will go through the basics of text mining, clarify its different methods and techniques, and make it easier to understand how it works. We implemented Latent Dirichlet Allocation techniques for mining the data from the data set; it works properly and will be in future development data mining techniques.

Modeling document labels using Latent Dirichlet allocation for archived documents in Integrated Quality Assurance System (IQAS)

“…It also emphasizes facts and relationships from large data sets (Yehia et al, 2016). This information is extracted and converted into structured data for visualization, analysis, and integration as structured data and refines the information using machine-learning methods (Gnanavel et al, 2022).…”

WES-BTM: A Short Text-Based Topic Clustering Model

“…This has led to difficulty in using traditional data processing and analysis means to meet the current needs. In the face of this challenge, text mining has gradually become a mainstream method for text information analysis, as it is capable of extracting key information from text datasets [7]. Among the many techniques of text mining, topic modeling is particularly remarkable [8].…”

“…A higher degree of topic consistency means that the correlation between topic words is stronger and more explanatory. The formula is shown in Equation (7).…”