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Research Article | Open Access

Visual exploration of Internet news via sentiment score and topic models

Zhejiang University, Hangzhou, China
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Abstract

Analyzing and understanding Internet news are important for many applications, such as market sentiment investigation and crisis management. However, it is challenging for users to interpret a massive amount of unstructured text, to dig out its accurate meaning, and to spot noteworthy news events. To overcome these challenges, we propose a novel visualization-driven approach for analyzing news text. We first collect Internet news from different sources and encode sentences into a vector representation suitable for input to a neural network, which calculates a sentiment score, to help detect news event patterns. A subsequent interactive visualization framework allows the user to explore the development of and relationships between Internet news topics. In addition, a method for detecting news events enables users and domain experts to interactively explore the correlations between market sentiment, topic distribution, and event patterns. We use this framework to provide a web-based interactive visualization system. We demonstrate the applicability and effectiveness of our proposed system using case studies involving blockchainnews.

Keywords

Internet news visualizationsentiment scoretopic modelsevent detection

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CVM_2020_3_333-347_ESM.mp4

References

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Computational Visual Media
Volume 6 Issue 3,
September 2020
Pages 333-347
DOI: 10.1007/s41095-020-0178-4
Cite this article:
Han S, Ye S, Zhang H. Visual exploration of Internet news via sentiment score and topic models. Computational Visual Media, 2020, 6(3): 333-347. https://doi.org/10.1007/s41095-020-0178-4

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Received: 14 March 2020
Accepted: 30 April 2020
Published: 04 August 2020
© The Author(s) 2020

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