Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Psuedolabels for training Sentiment Classifiers makes the model
  generalize better across datasets   The problem statement addressed in this work is : For a public sentiment
classification API, how can we set up a classifier that works well on different
types of data, having limited ability to annotate data from across domains. We
show that given a large amount of unannotated data from across different
domains and pseudolabels on this dataset generated by a classifier trained on a
small annotated dataset from one domain, we can train a sentiment classifier
that generalizes better across different datasets.

20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics