ConToVi: Multi‐Party Conversation Exploration using Topic‐Space Views

El‐Assady, Mennatallah; Gold, Valentin; Acevedo, Carmela; Collins, Christopher; Keim, Daniel A.

doi:10.1111/cgf.12919

Cited by 59 publications

(47 citation statements)

References 30 publications

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“…Most of such techniques support overview of sentiment value series over time with representations such as standard line plot [MDR06, DBG12, HHM*16] or river/stream graph [DQP*12, LZX*13, WLY*14, ZGWZ14]. Other popular approaches to visualize temporal sentiment data involve glyphs aligned along a timeline [WRM*09, WWS12, LKT*14] and animation‐based methods [GOCD12, ZLW13, EAGA*16].…”

Section: Sentiment Visualization Techniquesmentioning

confidence: 99%

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The State of the Art in Sentiment Visualization

Kucher

Paradis

Kerren

2017

Computer Graphics Forum

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Section: Sentiment Visualization Techniquesmentioning

confidence: 99%

“…Finally, El‐Assady et al . [EAGA*16] provide an animation‐based visualization of conversation transcripts, e.g. political debates transcripts, with their system ConToVi.…”

Section: Sentiment Visualization Techniquesmentioning

confidence: 99%

The State of the Art in Sentiment Visualization

Kucher

Paradis

Kerren

2017

Computer Graphics Forum

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“…Using this metaphor, we created a set of different (static and animated) views to highlight the various aspects of the speaker interactions. Figure 6 displays one time-frame of the utterance sedimentation view (El-Assady et al, 2016) of the accumulated presidential debates. In this animation, all discussed topics (ordered by their similarity to a selected base-topic at 12 o'clock) span the radial topic space.…”

Section: Speaker Analysis Viewsmentioning

confidence: 99%

Interactive Visual Analysis of Transcribed Multi-Party Discourse

El‐Assady

Hautli-Janisz

Gold

et al. 2017

Proceedings of ACL 2017, System Demonstrations

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We present the first web-based Visual Analytics framework for the analysis of multi-party discourse data using verbatim text transcripts. Our framework supports a broad range of server-based processing steps, ranging from data mining and statistical analysis to deep linguistic parsing of English and German. On the client-side, browser-based Visual Analytics components enable multiple perspectives on the analyzed data. These interactive visualizations allow exploratory content analysis, argumentation pattern review and speaker interaction modeling.

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“…Their visualization technique supports animation over the temporal progression of the visualized data per entity and shows the corresponding relations popping up over time. Similar, the work of El‐Assady et al utilizes animated display of topics or speakers over time to illustrate their evolution in conversations [EGA*16].…”

Section: Related Workmentioning

confidence: 99%

Interactive Ambiguity Resolution of Named Entities in Fictional Literature

Stoffel

Jentner

Behrisch

et al. 2017

Computer Graphics Forum

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Named entity recognition (NER) denotes the task to detect entities and their corresponding classes, such as person or location, in unstructured text data. For most applications, state of the art NER software is producing reasonable results. However, as a consequence of the methodological limitations and the well‐known pitfalls when analyzing natural language data, the NER results are likely to contain ambiguities. In this paper, we present an interactive NER ambiguity resolution technique, which enables users to create (post‐processing) rules for named entity recognition data based on the content and entity context of the analyzed documents. We specifically address the problem that in use‐cases where ambiguities are problematic, such as the attribution of fictional characters with traits, it is often unfeasible to train models on custom data to improve state of the art NER software. We derive an iterative process model for improving NER results, show an interactive NER ambiguity resolution prototype, illustrate our approach with contemporary literature, and discuss our work and future research.

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ConToVi: Multi‐Party Conversation Exploration using Topic‐Space Views

Cited by 59 publications

References 30 publications

The State of the Art in Sentiment Visualization

The State of the Art in Sentiment Visualization

Interactive Visual Analysis of Transcribed Multi-Party Discourse

Interactive Ambiguity Resolution of Named Entities in Fictional Literature

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