Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion
  Analysis   In this paper, we propose a two-layered multi-task attention based neural
network that performs sentiment analysis through emotion analysis. The proposed
approach is based on Bidirectional Long Short-Term Memory and uses
Distributional Thesaurus as a source of external knowledge to improve the
sentiment and emotion prediction. The proposed system has two levels of
attention to hierarchically build a meaningful representation. We evaluate our
system on the benchmark dataset of SemEval 2016 Task 6 and also compare it with
the state-of-the-art systems on Stance Sentiment Emotion Corpus. Experimental
results show that the proposed system improves the performance of sentiment
analysis by 3.2 F-score points on SemEval 2016 Task 6 dataset. Our network also
boosts the performance of emotion analysis by 5 F-score points on Stance
Sentiment Emotion Corpus.

18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex