SentiStory: multi-grained sentiment analysis and event summarization with crowdsourced social media data

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• 作者：Yi Ouyang ; Bin Guo ; Jiafan Zhang ; Zhiwen Yu…
• 关键词：Multi ; grained analysis ; Sentiment analysis ; Crowdsourced event summarization ; Sentiment change ; Social media
• 刊名：Personal and Ubiquitous Computing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：21
• 期：1
• 页码：97-111
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：User Interfaces and Human Computer Interaction; Computer Science, general; Personal Computing; Mobile Computing;
• 出版者：Springer London
• ISSN：1617-4917
• 卷排序：21

文摘

The massive social media data bring timely, multi-dimensional and rich information. Recently, many researchers have worked on event summarization with crowdsourced social media data. While existing works mostly focus on text-based summary, they only summarize representative microblogs. Public sentiment for the event is also valuable; however, this is not explored in microblogging event summary. In this paper, we propose SentiStory, which is a multi-grained sentiment analysis and event summarization system that summarizes event from two levels: coarse-grained and fine-grained sentiment analysis. In coarse-grained analysis, it discovers microblogs which are important in sentiment, while in fine-grained analysis, it detects significant change of sentiment in the event and identifies which microblog causes the change. Specifically, the proposed system comprises two modules: (1) the microblog preprocessing module firstly reduces redundant information and extracts useful information from the microblog database, and then, it separates different aspects of the event and clusters the same aspect together in a clue. (2) The multi-grained sentiment analysis model analyzes microblogs from two levels: coarse-grained and fine-grained. We perform detailed experimental study on real dataset collected from Sina Weibo, and the results demonstrate the effectiveness of our approach.