Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Contextual and Position-Aware Factorization Machines for Sentiment
  Classification   While existing machine learning models have achieved great success for
sentiment classification, they typically do not explicitly capture
sentiment-oriented word interaction, which can lead to poor results for
fine-grained analysis at the snippet level (a phrase or sentence).
Factorization Machine provides a possible approach to learning element-wise
interaction for recommender systems, but they are not directly applicable to
our task due to the inability to model contexts and word sequences. In this
work, we develop two Position-aware Factorization Machines which consider word
interaction, context and position information. Such information is jointly
encoded in a set of sentiment-oriented word interaction vectors. Compared to
traditional word embeddings, SWI vectors explicitly capture sentiment-oriented
word interaction and simplify the parameter learning. Experimental results show
that while they have comparable performance with state-of-the-art methods for
document-level classification, they benefit the snippet/sentence-level
sentiment analysis.

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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
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The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: physics
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ph
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The categories of this paper are: mtrl-th