Christine Kuang - Topic-Sentiment Model with Document-Level Covariates
Thursday, February 16, 2017 3:00 PM - 4:00 PM EST
(Joint work with Bin Yu and Jas Sekhon)
Text data analysis is becoming increasingly important with the rapid growth of text data. Two methods of text analysis are topical analysis and sentiment analysis. Topical analysis aims to detect the topics covered in a collection of documents. Sentiment analysis aims to detect opinions, feelings, and general sentiments expressed in text. Both have equally valuable applications in making inference about social and political cultures, attitudes, and processes. This project proposes a statistical model of text based on the Structural Topic Model (STM) which simultaneously detects both topic and sentiment. The proposed model differs in two aspects from existing topic-sentiment models: the data generating process and the ability to use document-level covariates for improved estimation of topics and sentiments as well as resulting inferences.