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Book Sections Year : 2015

Visual analysis of topics in Twitter based on co-evolution of terms

Lambert Pepin
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Julien Blanchard
Fabrice Guillet
Pascale Kuntz
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Philippe Suignard


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 x time x 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 data sets 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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hal-01111529 , version 1 (30-01-2015)


  • HAL Id : hal-01111529 , version 1


Lambert Pepin, Julien Blanchard, Fabrice Guillet, Pascale Kuntz, Philippe Suignard. Visual analysis of topics in Twitter based on co-evolution of terms. Berthold Lausen, Sabine Krolak-Schwerdt, Matthias Böhmer. Data Science, Learning by Latent Structures, and Knowledge Discovery, Springer, 2015, Studies in Classification, Data Analysis, and Knowledge Organization, 978-3-662-44982-0. ⟨hal-01111529⟩
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