Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Aspect Sentiment Analysis with Latent Sentiment-Aspect Attribution   In this paper, we introduce a new framework called the sentiment-aspect
attribution module (SAAM). SAAM works on top of traditional neural networks and
is designed to address the problem of multi-aspect sentiment classification and
sentiment regression. The framework works by exploiting the correlations
between sentence-level embedding features and variations of document-level
aspect rating scores. We demonstrate several variations of our framework on top
of CNN and RNN based models. Experiments on a hotel review dataset and a beer
review dataset have shown SAAM can improve sentiment analysis performance over
corresponding base models. Moreover, because of the way our framework
intuitively combines sentence-level scores into document-level scores, it is
able to provide a deeper insight into data (e.g., semi-supervised sentence
aspect labeling). Hence, we end the paper with a detailed analysis that shows
the potential of our models for other applications such as sentiment snippet
extraction.

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The categories of this paper are: nlin
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
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The categories of this paper are: eess
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat