题　目：A Structural Topic and Sentiment-Discourse Model for Text Analysis

嘉　宾：陈力，教授，康奈尔大学

主持人：江浦平 助理教授 上海交通大学安泰经济与管理学院

时　间：2024年6月12日（周三）14:00-15:30pm

地　点：安泰楼A511室

内容简介：

We consider the common setting where one observes a large number of opinionated text documents and related covariates, such as the text of online reviews along with the date of the review and the author demographic information. In this setting it can be of interest to understand how the covariates determine the text composition as well as the prevalence, sentiment, and/or discourse of various discussion themes. Yet, most topic modeling methods in the machine learning literature are designed to summarize the text for the purpose of exploratory analysis, not to perform this type of formal statistical inference. Further, topic modeling methods generally do not try to estimate the sentiment or discourse of discussion along separate topics which can be critical in business applications (e.g., for summarizing service or product quality). We develop a topic model called the Structural Topic and Sentiment-Discourse (STS) model that introduces a new document-level latent variable that captures the sentiment and/or discourse (termed as “sentiment-discourse”) for each topic, which modulates the word frequency within a topic. These latent topic sentiment-discourse variables are controlled by document-level covariates to allow for experimental control and regression analysis. We also introduce new computational methods to resolve scalability issues that have forced previous models to restrict to a small number of categorical covariates. We benchmark the STS model on three real-world datasets from surveys, blogs, and Yelp restaurant reviews around the coronavirus disease (COVID-19) pandemic. Our model recovers meaningful results including rich insights about how COVID-19 affects online reviews, demonstrating that the STS model can be useful for regression analysis with text data in addition to topic modeling’s traditional use of descriptive analysis.

演讲人简介：

Li Chen is the Emerson Professor of Manufacturing Management and Professor of Operations, Technology, and Information Management in the Samuel Curtis Johnson Graduate School of Management, part of the Cornell SC Johnson College of Business. He is currently serving as the Area Chair for Operations, Technology, and Information Management in the Cornell SC Johnson College of Business. His research interests include supply chain management, operations strategy, and data-driven analytics. Prior to his academic appointments, he was a cofounder and lead scientist at TrueDemand Software. He obtained his PhD in Management Science and Engineering from Stanford University and his BS in Automatic Control from Shanghai Jiao Tong University.

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