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Erschienen in:

2015 | OriginalPaper | Buchkapitel

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

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

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

Verlag: 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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Titel: Visual Analysis of Topics in Twitter Based on Co-evolution of Terms
verfasst von: Lambert Pépin
Julien Blanchard
Fabrice Guillet
Pascale Kuntz
Philippe Suignard
Verlag: Springer Berlin Heidelberg
Buch: Data Science, Learning by Latent Structures, and Knowledge Discovery
Print ISBN: 978-3-662-44982-0

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

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-662-44983-7_15

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