The Stor-e-Motion Visualization for Topic Evolution Tracking in Text Data Streams

Abstract

Abstract:Nowadays, there are plenty of sources generating massive amounts of text data streams in a continuous way. For example, the increasing popularity and the active use of social networks result in voluminous and fastflowing text data streams containing a large amount of user-generated data about almost any topic around the world. However, the observation and tracking of the ongoing evolution of topics in these unevenly distributed text data streams is a challenging task for analysts, news reporters, or other users. This paper presents "Store-Motion" a shape-based visualization to track the ongoing evolution of topics' frequency (i.e., importance), sentiment (i.e., emotion), and context (i.e., story) in user-defined topic channels over continuous flowing text data streams. The visualization supports the user in keeping the overview over vast amounts of streaming data and guides the perception of the user to unexpected and interesting points or periods in the text data stream. In this work, we mainly focus on the visualization of text streams from the social microblogging service Twitter, for which we present a series of case studies (e.g., the observation of cities, movies, or natural disasters) applied on real-world data streams collected from the public timeline. However, to further evaluate our visualization, we also present a baseline case study applied on the text stream of a fantasy book series.

A Survey of Opinion Mining in Arabic

“…Besides the typical types of charts and plots, visualizations inspired from the real world were also adapted to represent sentiment and dashboards were introduced to link multiple visualizations: a river stream graph [282,388], rectangular glyph [389], DNA visual shape [152], timeline arrangement with bubble charts [27], and, last, ring shapes [385].…”

The State of the Art in Sentiment Visualization

“…Finally, Weiler et al . [WGS15] apply their text stream analysis and visualization system called Stor‐e‐Motion for a combined text of the whole ‘Harry Potter’ series. The resulting visualization represents sentiment as a river‐like stream graph with an overlay of salient topic terms list for each time interval (in this particular case, position in text is treated as timestamp).…”

“…Quite few sentiment visualization techniques (16%) support the task of Monitoring , which could be described as dynamic observation and alerts on changes in the data. Some of the techniques use a continuously updated timeline metaphor to represent the changes in data [KRHW12, KSP*15, WGS15], others involve dedicated representations heavily relying on animation [GAP*10, GOCD12, EAGA*16, KJDP16], or combine multiple representations [MBB*11, CQZ*12, SHHJ*16]. Calderon et al .…”

“…The author uses multiple basic representations such as line plots, bar charts, and word clouds to analyze the distribution and temporal trends of emotion-bearing word usage in individual documents and corpora. Finally, Weiler et al [WGS15] apply their text stream analysis and visualization system called Stor-e-Motion for a combined text of the whole 'Harry Potter' series. The resulting visualization represents sentiment as a river-like stream graph with an overlay of salient topic terms list for each time interval (in this particular case, position in text is treated as timestamp).…”