Topic-based content and sentiment analysis of Ebola virus on Twitter and in the news
Topic-based content and sentiment analysis of Ebola virus on Twitter and in the news
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DOI：
10.1177/0165551515608733
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发表时间：
2016-12-01
影响因子：
2.4
通讯作者：
Song, Min
中科院分区：
文献类型：
--
作者：
Kim, Erin Hea-Jin;Jeong, Yoo Kyung;Song, Min
本研究调查了两种不同媒体来源（推特和新闻出版物）关于埃博拉这一热门健康问题的主题报道和情绪动态。我们通过以下方式进行内容和情感分析：（1）对收集的数据集应用词汇控制； (2)采用n-gram LDA主题建模技术； (3)采用实体抽取和实体网络； (4)引入基于主题的情感评分的概念。通过查询术语“埃博拉”或“埃博拉病毒”，我们通过 Twitter 流 API 收集了来自 1006 个不同出版物的 16,189 篇新闻文章和 7,106,297 条推文。实验表明，Twitter 的话题覆盖范围比新闻媒体更窄、更模糊。从情绪动态来看，Twitter 上情绪的寿命和方差比新闻中的更短、更小。此外，我们观察到新闻文章更关注与事件相关的实体，例如人物、组织和位置，而 Twitter 则涵盖更多面向时间的实体。根据结果，我们报告了 Twitter 和新闻媒体作为两个不同新闻媒体在内容覆盖和情绪动态方面的特征。
The present study investigates topic coverage and sentiment dynamics of two different media sources, Twitter and news publications, on the hot health issue of Ebola. We conduct content and sentiment analysis by: (1) applying vocabulary control to collected datasets; (2) employing the n-gram LDA topic modeling technique; (3) adopting entity extraction and entity network; and (4) introducing the concept of topic-based sentiment scores. With the query term 'Ebola' or 'Ebola virus', we collected 16,189 news articles from 1006 different publications and 7,106,297 tweets with the Twitter stream API. The experiments indicate that topic coverage of Twitter is narrower and more blurry than that of the news media. In terms of sentiment dynamics, the life span and variance of sentiment on Twitter is shorter and smaller than in the news. In addition, we observe that news articles focus more on event-related entities such as person, organization and location, whereas Twitter covers more time-oriented entities. Based on the results, we report on the characteristics of Twitter and news media as two distinct news outlets in terms of content coverage and sentiment dynamics.