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Interactive Visual Analysis of Temporal Text Data

Published: 24 August 2015 Publication History

Abstract

This paper presents a novel interactive visualization technique that helps in gathering insights from large volumes of text generated through dyadic communications. The emphasis is specifically on showing content evolution and modification with passage of time. The challenge lies in presenting not only the content as a stand-alone but also understand how the present is related to the past. For example analyzing large volumes of emails can show how communication among a set of people have progressed or evolved over time, may be along with the roles of the communicators. It can also show how the content has changed or evolved. In order to depict the changes, the email repositories are first clustered using a novel algorithm. The clusters are further time-stamped and correlated. User-insights are provided through visualization of these clusters. Results of implementation over two different datasets are presented.

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  • (2021)A Systematic Review on Dyadic Conversation VisualizationsCompanion Publication of the 2021 International Conference on Multimodal Interaction10.1145/3461615.3485396(137-147)Online publication date: 18-Oct-2021

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VINCI '15: Proceedings of the 8th International Symposium on Visual Information Communication and Interaction
August 2015
185 pages
ISBN:9781450334822
DOI:10.1145/2801040
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 August 2015

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Author Tags

  1. Information Visualization
  2. Text analytics
  3. Topic Extraction

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  • Research-article
  • Research
  • Refereed limited

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VINCI '15

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VINCI '15 Paper Acceptance Rate 12 of 32 submissions, 38%;
Overall Acceptance Rate 71 of 193 submissions, 37%

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Cited By

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  • (2021)A Systematic Review on Dyadic Conversation VisualizationsCompanion Publication of the 2021 International Conference on Multimodal Interaction10.1145/3461615.3485396(137-147)Online publication date: 18-Oct-2021

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