Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Learning of Poisson Factorisation Model for Gauging Brand
  Sentiment in User Reviews   In this paper, we propose the Brand-Topic Model (BTM) which aims to detect
brand-associated polarity-bearing topics from product reviews. Different from
existing models for sentiment-topic extraction which assume topics are grouped
under discrete sentiment categories such as `positive', `negative' and
`neural', BTM is able to automatically infer real-valued brand-associated
sentiment scores and generate fine-grained sentiment-topics in which we can
observe continuous changes of words under a certain topic (e.g., `shaver' or
`cream') while its associated sentiment gradually varies from negative to
positive. BTM is built on the Poisson factorisation model with the
incorporation of adversarial learning. It has been evaluated on a dataset
constructed from Amazon reviews. Experimental results show that BTM outperforms
a number of competitive baselines in brand ranking, achieving a better balance
of topic coherence and uniqueness, and extracting better-separated
polarity-bearing topics.

18
The categories of this paper are: econ
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg