Readability Visualization for Massive Text Data

TL;DR: This study suggests readability measurement factors and formulas for the suggested methods that visualize texts by extracting a key factor ‘length’ for readability and verifies effectiveness of visualization through the test of the case studies.

Abstract: In general, people read texts and decide by themselves to measure levels of understandability and readability, which takes a lot of time and efforts. We believe visualizing readability gives intuitive impact on how difficult the texts will be before examining the texts further. Text visualization aims to provide structural characteristics of text contents in an efficient way. By using massive text data, such as books or documents, this study suggests readability measurement factors and formulas for the suggested methods that visualize texts by extracting a key factor ‘length’ for readability. In addition to the proposed methods, this study verifies effectiveness of visualization through the test of the case studies. The paper also includes case study findings that readers can have readability information not from independent texts, but from the comparison of previous texts, and therefore it becomes easier to accommodate difficult level of new books.