Processing online news streams for large-scale semantic analysis

Krstajic, Milos; Mansmann, Florian; Stoffel, Andreas; Atkinson, Martin; Keim, Daniel A.

doi:10.1109/icdew.2010.5452710

Cited by 16 publications

(10 citation statements)

References 12 publications

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“…Here, we list the categories with some references to prominent examples. Data sources include the following self-evident items: Document [33], Corpora [25], and Streams [19]. The special data properties include Geospatial [11], Timeseries [14], and…”

Section: Data and Visualizationmentioning

confidence: 99%

Text visualization techniques: Taxonomy, visual survey, and community insights

Kucher

Kerren

2015

2015 IEEE Pacific Visualization Symposium (PacificVis)

155

129

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Text visualization has become a growing and increasingly important subfield of information visualization. Thus, it is getting harder for researchers to look for related work with specific tasks or visual metaphors in mind. In this paper, we present an interactive visual survey of text visualization techniques that can be used for the purposes of search for related work, introduction to the subfield and gaining insight into research trends. We describe the taxonomy used for categorization of text visualization techniques and compare it to approaches employed in several other surveys. Finally, we present results of analyses performed on the entries data.

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Section: Data and Visualizationmentioning

confidence: 99%

Text visualization techniques: Taxonomy, visual survey, and community insights

Kucher

Kerren

2015

2015 IEEE Pacific Visualization Symposium (PacificVis)

155

129

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“…So-called History Flows are used by Viégas et al [2004] to track collaborative authoring. Krstajic et al [2010] visualize daily aggregates of entity occurrences in news with stacked time series.…”

Section: Visual Text Time-series Analysismentioning

confidence: 99%

Feature-Based Visual Sentiment Analysis of Text Document Streams

Rohrdantz

Hao

Dayal

et al. 2012

ACM Trans. Intell. Syst. Technol.

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This article describes automatic methods and interactive visualizations that are tightly coupled with the goal to enable users to detect interesting portions of text document streams. In this scenario the interestingness is derived from the sentiment, temporal density, and context coherence that comments about features for different targets (e.g., persons, institutions, product attributes, topics, etc.) have. Contributions are made at different stages of the visual analytics pipeline, including novel ways to visualize salient temporal accumulations for further exploration. Moreover, based on the visualization, an automatic algorithm aims to detect and preselect interesting time interval patterns for different features in order to guide analysts. The main target group for the suggested methods are business analysts who want to explore time-stamped customer feedback to detect critical issues. Finally, application case studies on two different datasets and scenarios are conducted and an extensive evaluation is provided for the presented intelligent visual interface for feature-based sentiment exploration over time.

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“…Such representation is appropriate rather for professionals. Krstajic et al [20] used a similar way of visualization, but instead of visualizing theme streams, they visualized streams of entities (an organisation or a person) and their relative share within news articles over a time period using a linear time scale.…”

Section: News Visualizationmentioning

confidence: 99%

“…), but more often the time dimension is visualized using a timeline oriented from left to right (as with ThemeRiver TM [19] or work of Krstajic et al [20]). Such timeline forms the base of our visualization model as well.…”

Section: Story Visualizationmentioning

confidence: 99%

Multiple developing news stories identified and tracked by social insects and visualized using the new galactic streams and concurrent streams metaphors

Sabo

Kovarova

Návrat

2015

HIS

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We have developed an approach to identification and tracking of currently unfolding news stories extracted from the news articles published on the Web. Our approach employs a set of agents to retrieve those articles from the Web that might refer to some developing news story. The set of agents is inspired by social insects, in particular by a bee colony. Bees identify popular terms, referred to as story words, relevant to the ongoing news stories and use them in foraging articles. This allows for a dynamic approach that reflects the changes in article space as new stories unfold and new articles are added. Subsequently a graph representation of the article space is constructed that contains retrieved articles and identified story words interconnected by edges according to relationships of relevance identified between elements of the graph. Stories are then extracted from the constructed graph by using Louvain method, commonly used to identify communities within modular graphs. Using this approach we have been able to identify news stories in a stream of articles retrieved from the Web with precision of 75.56%, with best precision generally achieved for recent news stories described by popular story words. Further we developed ways of visualization of multiple stories represented by sets of articles ordered in time. We propose two new metaphors both employing an exponential timeline. Both galactic streams and concurrent streams are highly suitable for visualizing multiple developing stories.

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Processing online news streams for large-scale semantic analysis

Cited by 16 publications

References 12 publications

Text visualization techniques: Taxonomy, visual survey, and community insights

Text visualization techniques: Taxonomy, visual survey, and community insights

Feature-Based Visual Sentiment Analysis of Text Document Streams

Multiple developing news stories identified and tracked by social insects and visualized using the new galactic streams and concurrent streams metaphors

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