sDTM:用于文本分析的监督贝叶斯深度主题模型

Yi Yang, Kunpeng Zhang, Yangyang Fan
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引用次数: 12

摘要

本研究提出了一种新的监督深度主题建模方法,用于有效的文本分析。这种方法利用与文本相关的辅助数据,例如消费者评论中的评分或在线论坛中帖子的类别,来增强对文本中潜在主题的发现。该方法可以从多个方面有效地提高主题建模性能。首先,学习到的潜在主题更有意义和可区分,这有助于文本数据的探索。其次,新监督深度主题模型发现的潜在主题更加准确,从而提高了利用潜在主题作为输入的下游计量经济学和预测分析的性能。鉴于辅助数据在现实世界文本分析任务中的普遍存在以及主题建模在商业研究和实践中的广泛采用,本研究为从文本数据中提取见解提供了有效的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics
This study proposes a novel supervised deep topic modeling approach for effective text analysis. This approach leverages the auxiliary data associated with text, such as ratings in consumer reviews or categories of posts in online forums, to enhance the discovery of latent topics in text. The proposed approach can effectively improve topic modeling performance in several ways. First, the learned latent topics are more meaningful and distinguishable, which helps text data exploration. Second, the latent topics discovered by the novel supervised deep topic model are more accurate, which improves the performance of downstream econometrics and predictive analytics that utilize latent topics as inputs. Given the prevalence of auxiliary data in real-world text analysis tasks and the wide adoption of topic modeling in business research and practice, the study offers an effective solution for extracting insights from text data.
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