对三种流行的计算语言学方法进行批判性反思,以检查Twitter话语
Dan Heaton1, Jeremie Clos1, Elena Nichele1
1School of Computer Science, University of Nottingham, Nottingham, United Kingdom.
PeerJ. Computer science
|June 22, 2023
概括
计算语言方法提供了对在线话语的洞察力,但需要透明的应用. 这项研究批判性地检查了Twitter数据上的主题建模,情绪和情绪检测,突出了解释挑战,并提出了综合的定性方法.
科学领域:
- 计算语言学计算语言学
- 社交媒体研究 社交媒体研究
- 数字话语分析 数字话语分析
背景情况:
- 诸如主题建模,情感分析和情感检测等计算语言方法对于分析在线公共话语非常有价值.
- 现有的文献突出了这些方法的优点和缺点,但缺乏明确的指令,用于批判性应用.
研究的目的:
- 批判性地评价计算语言学方法的应用在分析二次性推特话语.
- 在现实世界的案例研究中识别解释和应用这些方法的挑战.
- 为社交媒体研究提出一个更加透明和综合的框架.
主要方法:
- 应用主题建模,情感分析和情绪检测到Twitter数据.
- 分析了两项英国的案例研究:NHS Covid-19数字联系人追踪应用程序演讲和Ofqual A Level等级计算算法演讲.
- 对方法应用进行批判性反思,重点关注期望设定,轨迹呈现,上下文检查和二次性分析.
主要成果:
- 在所有三种计算方法中都发现了解释和潜在应用方面的困难.
- 观察到特定的挑战,包括检测否定和刺.
- 该研究强调,对于社交媒体研究的计算语言学方法需要更大的透明度.
结论:
- 计算语言方法需要在社交媒体研究中仔细和透明地应用.
- 将计算方法与语料库语言学和批判性话语分析等定性方法相结合,可以提高分析严谨性.
- 进一步开发综合框架是必要的,以进行强大的社交媒体话语分析.
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