Visual exploration of Internet news via sentiment score and topic models

Computational Visual Media

Songye Han¹  Shaojie Ye¹  Hongxin Zhang¹ 

[1] Zhejiang University, Hangzhou, China;

关键词: Internet news visualization;    sentiment score;    topic models;    event detection;   

DOI  :  10.1007/s41095-020-0178-4

来源: Springer

【 摘 要 】

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 blockchain news.

【 授权许可】

CC BY