Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semisupervised Autoencoder for Sentiment Analysis   In this paper, we investigate the usage of autoencoders in modeling textual
data. Traditional autoencoders suffer from at least two aspects: scalability
with the high dimensionality of vocabulary size and dealing with
task-irrelevant words. We address this problem by introducing supervision via
the loss function of autoencoders. In particular, we first train a linear
classifier on the labeled data, then define a loss for the autoencoder with the
weights learned from the linear classifier. To reduce the bias brought by one
single classifier, we define a posterior probability distribution on the
weights of the classifier, and derive the marginalized loss of the autoencoder
with Laplace approximation. We show that our choice of loss function can be
rationalized from the perspective of Bregman Divergence, which justifies the
soundness of our model. We evaluate the effectiveness of our model on six
sentiment analysis datasets, and show that our model significantly outperforms
all the competing methods with respect to classification accuracy. We also show
that our model is able to take advantage of unlabeled dataset and get improved
performance. We further show that our model successfully learns highly
discriminative feature maps, which explains its superior performance.

8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol