Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Review-Level Sentiment Classification with Sentence-Level Polarity
  Correction   We propose an effective technique to solving review-level sentiment
classification problem by using sentence-level polarity correction. Our
polarity correction technique takes into account the consistency of the
polarities (positive and negative) of sentences within each product review
before performing the actual machine learning task. While sentences with
inconsistent polarities are removed, sentences with consistent polarities are
used to learn state-of-the-art classifiers. The technique achieved better
results on different types of products reviews and outperforms baseline models
without the correction technique. Experimental results show an average of 82%
F-measure on four different product review domains.

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The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
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The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: astro-ph
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th