Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Implicit Sentiment in Aspect-based Sentiment Analysis with
  Supervised Contrastive Pre-Training   Aspect-based sentiment analysis aims to identify the sentiment polarity of a
specific aspect in product reviews. We notice that about 30% of reviews do not
contain obvious opinion words, but still convey clear human-aware sentiment
orientation, which is known as implicit sentiment. However, recent neural
network-based approaches paid little attention to implicit sentiment entailed
in the reviews. To overcome this issue, we adopt Supervised Contrastive
Pre-training on large-scale sentiment-annotated corpora retrieved from
in-domain language resources. By aligning the representation of implicit
sentiment expressions to those with the same sentiment label, the pre-training
process leads to better capture of both implicit and explicit sentiment
orientation towards aspects in reviews. Experimental results show that our
method achieves state-of-the-art performance on SemEval2014 benchmarks, and
comprehensive analysis validates its effectiveness on learning implicit
sentiment.

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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: stat
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The categories of this paper are: atom-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: q-fin