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对使用主题模型进行简短文本社交媒体分析的系统审查

Caitlin Doogan Poet Laureate¹, Wray Buntine², Henry Linger¹

¹Faculty of IT, Monash University, Wellington Rd, Clayton, VIC 3800 Australia.

Artificial intelligence review

|June 26, 2023

概括

研究人员经常滥用短文主题模型进行社交媒体分析. 这项研究揭示了模型开发和用户需求之间的差距,突出了低于最佳的使用和缺乏解释主题的指导.

科学领域:

计算社会科学计算社会科学
自然语言处理自然语言处理.
信息检索信息检索

背景情况:

短文本主题模型面临着社交媒体数据的挑战,目前的自动评估可能不会产生有意义的见解.
目前关于主题模型评估和研究人员实践的现有研究是有限的,可能会影响研究结果的有效性.
了解研究人员如何在应用社交媒体分析中利用主题模型对于改进方法论至关重要.

研究的目的:

系统地审查和理解主题模型在社交媒体分析中的应用.
确定研究人员如何以及为什么使用社交媒体数据的主题模型.
为了发现主题模型开发和应用研究人员的需求之间的错位.

主要方法:

进行了对189篇文章的系统文献审查,使用主题建模进行社交媒体分析.
分析了使用模式,并确定了研究人员在构建和解释主题时所面临的挑战.
检查了自动化评估指标与实际研究需求之间的一致性.

主要成果:

主题模型开发与社交媒体分析的实际要求不一致.
研究人员经常以次优化方式使用主题模型,表明缺乏方法论支持.
在建立和解释来自社交媒体数据的主题的指导方面存在很大的差距.

结论:

目前对社交媒体分析的短文话题模型的开发和应用是不理想的.
提供了建议,以弥合主题模型开发和应用研究需求之间的差距.
改善方法论支持对于在社交媒体研究中有效和有洞察力的话题建模至关重要.

关键词:

在 LDA LDA 中.在NLP中,我们使用了NLP.一个简短的短文本.社交媒体社交媒体主题模型主题模型主题模型他们的推特是Twitter.

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