美国文学 基于计算机网络技术的新闻叙述
PloS one
|October 16, 2023
概括
这项研究使用术语频率-反向文档频率 (TF-IDF) 和文本分类从网页提取纯新闻内容. 它识别了文学新闻中的叙事结构,以提高可读性和读者的理解力.
科学领域:
- 信息科学 信息科学 信息科学
- 计算语言学 计算语言学
- 媒体研究 媒体研究
背景情况:
- 在线新闻传播很普遍,但被冗余的网络内容 (广告,导航) 阻碍.
- 由于外部信息,用户难以访问纯粹的新闻内容.
- 有效的新闻提取和叙事分析对于改善在线新闻消费至关重要.
研究的目的:
- 开发一个系统,从多余的网页中提取纯新闻内容.
- 分析美国文学新闻报道的叙事特征.
- 通过有效的叙事策略,提高新闻可读性和读者参与度.
主要方法:
- 使用术语频率-反向文档频率 (TF-IDF) 用于关键字提取和内容识别.
- 使用文本分类算法 (SVM,KNN,AdaBoost) 进行新闻文本分类.
- 应用文档对象模型 (DOM) 技术用于分析网页结构并提取关键元素.
- 在15篇美国文学新闻报道中分析了叙事序列,持续时间和结构.
主要成果:
- 确定序列和闪回作为文学新闻中占主导地位的叙事序列.
- 发现叙事持续时间主要使用"胜利率"和"概要",并补充了场景和暂停.
- 确定53.3%的文学新闻结构是时间连接的,增强了背景和主角的理解.
- 开发了一个新闻提取系统,结合关键字特征和DOM树分析.
结论:
- 开发的系统有效地从杂乱的网页中提取纯新闻内容.
- 时间连接的叙事结构显著提高了文学新闻的可读性和理解性.
- 调查结果为媒体从业人员提供了关于新闻叙述技术和策略的宝贵见解.
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