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 blockchain news.
Highlights
Available information is growing ever more rapidly, and there is a particular requirement to interpret and extract key information from large quantitiesManuscript received: 2020-03-14; accepted: 2020-04-30 of Internet news data
To assess our bidirectional LSTM (BiLSTM)-based downstream model for computing sentiment score, we evaluated other classification models including Adaboost, a support vector machine (SVM), a random forest (RF), and a convolutional neural network (CNN)
Since the definition of a news event is often vague, and there is no open dataset of labeled Internet events for us to conduct supervised learning, we provide our event detection for exploratory purposes
Summary
Manuscript received: 2020-03-14; accepted: 2020-04-30 of Internet news data This has many applications, such as market sentiment investigation and crisis management. As well as other visualization tools, are effective in helping users to understand news. Our work aims to compute a sentiment score that summarises meaningful information in Internet news; we use it as a basis for event detection and pattern analysis. We provide a novel interactive visualization framework to help user such as investors and domain experts to better understand changes in sentiment from news. With this tool, they can explore patterns in sentiment polarity and better predict trends
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