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Modernising Official Statistics: A Complex Challenge Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Visual Analysis of Topics in Twitter Based on Co-evolution of Terms

Authors : Lambert Pépin, Julien Blanchard, Fabrice Guillet, Pascale Kuntz, Philippe Suignard

Published in: Data Science, Learning by Latent Structures, and Knowledge Discovery

Publisher: Springer Berlin Heidelberg

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Abstract

The analysis of Twitter short messages has become a key issue for companies seeking to understand consumer behaviour and expectations. However, automatic algorithms for topic tracking often extract general tendencies at a high granularity level and do not provide added value to experts who are looking for more subtle information. In this paper, we focus on the visualization of the co-evolution of terms in tweets in order to facilitate the analysis of the evolution of topics by a decision-maker. We take advantage of the perceptual quality of heatmaps to display our 3D data (term × time × score) in a 2D space. Furthermore, by computing an appropriate order to display the main terms on the heatmap, our methodology ensures an intuitive visualization of their co-evolution. An experiment was conducted on real-life datasets in collaboration with an expert in customer relationship management working at the French energy company EDF. The first results show three different kinds of co-evolution of terms: bursty features, reoccurring terms and long periods of activity.

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Footnotes
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Metadata
Title
Visual Analysis of Topics in Twitter Based on Co-evolution of Terms
Authors
Lambert Pépin
Julien Blanchard
Fabrice Guillet
Pascale Kuntz
Philippe Suignard
Copyright Year
2015
Publisher
Springer Berlin Heidelberg
Book
Data Science, Learning by Latent Structures, and Knowledge Discovery

Print ISBN: 978-3-662-44982-0

Electronic ISBN: 978-3-662-44983-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-662-44983-7_15

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