Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Semi-Supervised Approach to Detecting Stance in Tweets   Stance classification aims to identify, for a particular issue under
discussion, whether the speaker or author of a conversational turn has Pro
(Favor) or Con (Against) stance on the issue. Detecting stance in tweets is a
new task proposed for SemEval-2016 Task6, involving predicting stance for a
dataset of tweets on the topics of abortion, atheism, climate change, feminism
and Hillary Clinton. Given the small size of the dataset, our team created our
own topic-specific training corpus by developing a set of high precision
hashtags for each topic that were used to query the twitter API, with the aim
of developing a large training corpus without additional human labeling of
tweets for stance. The hashtags selected for each topic were predicted to be
stance-bearing on their own. Experimental results demonstrate good performance
for our features for opinion-target pairs based on generalizing dependency
features using sentiment lexicons.

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The categories of this paper are: plasm-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
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The categories of this paper are: math
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
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The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: stat
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The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph