Visual analysis of topics in Twitter based on co-evolution of terms (original) (raw)

HAL (Le Centre pour la Communication Scientifique Directe), 2015

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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