Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Entity-Specific Sentiment Classification of Yahoo News Comments   Sentiment classification is widely used for product reviews and in online
social media such as forums, Twitter, and blogs. However, the problem of
classifying the sentiment of user comments on news sites has not been addressed
yet. News sites cover a wide range of domains including politics, sports,
technology, and entertainment, in contrast to other online social sites such as
forums and review sites, which are specific to a particular domain. A user
associated with a news site is likely to post comments on diverse topics (e.g.,
politics, smartphones, and sports) or diverse entities (e.g., Obama, iPhone, or
Google). Classifying the sentiment of users tied to various entities may help
obtain a holistic view of their personality, which could be useful in
applications such as online advertising, content personalization, and political
campaign planning. In this paper, we formulate the problem of entity-specific
sentiment classification of comments posted on news articles in Yahoo News and
propose novel features that are specific to news comments. Experimental results
show that our models outperform state-of-the-art baselines.

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The categories of this paper are: q-bio
3
The categories of this paper are: physics
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The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
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The categories of this paper are: stat
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: supr-con
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The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph