A Picture for the Words! Textual Visualization in Big Data Analytics

TL;DR

This paper reviews and classifies various approaches to visualizing textual data in big data analytics, proposing a framework to guide appropriate graphical representation and avoid misinterpretation.

Contribution

It introduces an analytical classification framework for textual data visualization, helping to understand and select suitable graphical methods in big data contexts.

Findings

Identifies key textual data visualization approaches.

Classifies visualization methods based on quantity, sense, trend, and context.

Provides insights to prevent distorted data representations.

Abstract

Data Visualization has become an important aspect of big data analytics and has grown in sophistication and variety. We specifically identify the need for an analytical framework for data visualization with textual information. Data visualization is a powerful mechanism to represent data, but the usage of specific graphical representations needs to be better understood and classified to validate appropriate representation in the contexts of textual data and avoid distorted depictions of underlying textual data. We identify prominent textual data visualization approaches and discuss their characteristics. We discuss the use of multiple graph types in textual data visualization, including the use of quantity, sense, trend and context textual data visualization. We create an explanatory classification framework to position textual data visualization in a unique way so as to provide insights and assist in appropriate method or graphical representation classification.