基于韩国博客舆论挖掘的大众健康主流趋势挖掘

Yong-il Lee, Sang-Hyob Nam, Jaeseung Jeong
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引用次数: 1

摘要

如今,社交媒体通常成为理解公众思想的合理标准。特别是,人们越来越多地使用网络媒体和SNS (twitter、facebook、博客等)来分享观点、新闻、建议、兴趣、情绪、关注、批评、事实、谣言等等。因此,公共卫生研究已经开始了一个大的变化。传统的公共卫生研究仅依赖于卫生专业人员的定期临床报告。它仅限于实际使用,一般公众很难理解健康信息,即使是他/她自己的信息。如今,每分钟都有超过10亿人发表他们对许多话题的看法,包括健康状况。SNS为研究人员提供了全球范围内公共卫生状况的最新来源。其中大部分数据都是公开的,可供挖掘。因此,本文试图将意见挖掘应用于在海量信息中发现公众的动向和有价值的意见。本研究的核心是对意见形容词的分析。我们的假设是,许多形容词表达都隐含着作者深刻而真诚的意思。它既适用于低价值的帖子过滤,也适用于高价值的帖子跟踪。这种方法是一种简单可行的准则。意见挖掘过程包括韩语语素分析、意见提取、意见标注、正面/负面评分评价。我们的研究目的是分析韩国的博客文章。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mining the Main Health Trend of the General Public based on Opinion Mining of Korean Blogsphere
These days, social media usually becomes a reasonable standard for understanding the public's thought. Especially, people increasingly use internet media and SNS (twitter, facebook, blog, and etc.), to share opinions, news, advice, interests, moods, concerns, critics, facts, rumors, and everything. Therefore, public health research has been started a big change. Traditional public health study has depended on only regular clinical reports by health professionals. It is limited to practical use and general public has much difficulty to understand health information, even if it's his/her own information. Nowadays, over one billion people publish their ideas about many topics, including health conditions minute by minute. SNS provides researchers the freshest source of public health conditions on a global scale. Much of that data is public and available for mining. So this article pursues making an application of opinion mining for detecting the public's trend and finding valuable opinion among the massive information. The core of this research is analyzing the adjective of opinions. Our assumption is that many adjective expressions implicate deep and sincere meaning of its author. It is applicable for both low value postings filtering and tracking high value postings simultaneously. This approach is a simple and feasible criteria. The opinion mining process includes Korean morpheme analysis, opinion extraction, opinion tagging, positive / negative score evaluation. Our research's aim is to analyze Korean blog postings.
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