在Twitter上测量政治情绪：多项式逆回归的因子最优设计
Measuring political sentiment on Twitter: factor-optimal design for multinomial inverse regression
摘要：
本文介绍了文本分析中的一个简短案例研究：针对特定美国政客的积极，消极或中立情绪的Twitter帖子的评分。这项研究需要选择一个代表性职位的子样本进行情感评分，这是情感挖掘的一个常见且昂贵的方面。作为一个一般的贡献，我们的应用程序之前提出的算法最大限度地提高抽样效率。特别是，我们概述和说明贪婪选择的文件，以建立设计，是D-最优的主题因子分解的原始文本。该策略被应用到我们的激励数据集的政治职位，我们概述了一种新的技术，通过使用变量的相互作用在多项式逆回归预测通用和特定主题的文档情绪。结果是对2012年2月左右的210万条Twitter帖子进行分析的结果。
英文摘要：
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.
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