NDLI: Structure-Aware Visualization of Text Corpora

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Structure-Aware Visualization of Text Corpora

Content Provider	ACM Digital Library
Author	Siersdorfer, Stefan Zerr, Sergej Singh, Jaspreet
Abstract	Trying to comprehend the structure and content of large text corpora can be a daunting and often time consuming task. In this paper, we introduce a novel tool that exploits the structural properties for extracting and visualizing the underlying topics in a given dataset. To this end, we make use of a combination of latent topic analysis, discriminative feature selection applied on top of the category structure of corpora, and various ranking methods in order to extract the most representative topics for a given corpus. The visual moniker to depict the outcome of these methods can be chosen based on the context. Such visual representations can be useful for depicting trends, identifying ``hot'' topics, and discovering interesting patterns in the underlying data. As applications, we create example representations for a variety of corpora obtained from conference proceedings, movie summaries, and newsgroup postings. Our user experiments demonstrate the viability of our approach, with a flower-like visualization inspired by the ``wheel of emotion'', for generating high quality representative topics and for unearthing hidden structures and connections in large document corpora.
Starting Page	107
Ending Page	116
Page Count	10
File Format	PDF
ISBN	9781450346771
DOI	10.1145/3020165.3020182
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-03-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Flower Latent topics Visualizing topics Text corpora Lda Evenness index Visualizing structured text Mutual information
Content Type	Text
Resource Type	Article

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