Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Measuring political sentiment on Twitter: factor-optimal design for
  multinomial inverse regression   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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The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
1
The categories of this paper are: cs