2012/06/17 by Matt Taddy, Taddy, Matt
Computer Science · Mathematics · Social Sciences · #Advanced Text Analysis Techniques #Applications (stat.AP) #Computational and Text Analysis Methods #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining #Text and Document Classification Technologies #Topic Modeling #stat.AP
paper · pdf · doi:10.48550/arxiv.1206.3776
To appear in Technometrics. Code is available in the textir package for R
openalex publication_date 2012/06/17 · arxiv created 2013/03/02 · arxiv updated 2013/03/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This article presents a short case study in text analysis: the scoring of Twitter posts for positive, negative, or neutral sentiment directed towards particular US politicians. The study requires selection of a sub-sample of representative posts for sentiment scoring, a common and costly aspect of sentiment mining. As a general contribution, our application is preceded by a proposed algorithm for maximizing sampling efficiency. In particular, we outline and illustrate greedy selection of documents to build designs that are D-optimal in a topic-factor decomposition of the original text. The strategy is applied to our motivating dataset of political posts, and we outline a new technique for predicting both generic and subject-specific document sentiment through use of variable interactions in multinomial inverse regression. Results are presented for analysis of 2.1 million Twitter posts around February 2012.