Abstract
As people freely express their opinions toward a product on Twitter streams without being bound by time, visualizing time pattern of customers emotional behavior can play a crucial role in decision-making. We analyze how emotions are fluctuated in pattern and demonstrate how we can explore it into useful visualizations with an appropriate framework. We manually customized the current framework in order to improve a state-of-the-art of crawling and visualizing Twitter data. The data, post or update on status on the Twitter website about iPhone, was collected from U.S.A, Japan, Indonesia, and Taiwan by using geographical bounding-box and visualized it into two-dimensional heat map, interactive stream graph, and context focus via brushing visualization. The results show that our proposed system can explore uniqueness of temporal pattern of customers emotional behavior.
Highlights
With the rapid growth of social media information and vast amounts of information, how to deal with and to understand information becomes more and more difficult [1]
To meet the above-mentioned challenges, we propose the exploration of temporal data from Twitter stream by using two-dimensional heat map visualization, interactive stacked area chart, and context focus via brushing with its graphical interaction as well
We developed a text classification to judge customer‟s emotion from opinions in microblog
Summary
With the rapid growth of social media information and vast amounts of information, how to deal with and to understand information becomes more and more difficult [1]. Many previous related studies [13]-[16] introduced visualization for exploring the sentiments of time-varying Twitter data.
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