Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Latent Sentiment Detection in Online Social Networks: A
  Communications-oriented View   In this paper, we consider the problem of latent sentiment detection in
Online Social Networks such as Twitter. We demonstrate the benefits of using
the underlying social network as an Ising prior to perform network aided
sentiment detection. We show that the use of the underlying network results in
substantially lower detection error rates compared to strictly features-based
detection. In doing so, we introduce a novel communications-oriented framework
for characterizing the probability of error, based on information-theoretic
analysis. We study the variation of the calculated error exponent for several
stylized network topologies such as the complete network, the star network and
the closed-chain network, and show the importance of the network structure in
determining detection performance.

36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics