Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Learning of Semantic Orientation from a
  Hundred-Billion-Word Corpus   The evaluative character of a word is called its semantic orientation. A
positive semantic orientation implies desirability (e.g., "honest", "intrepid")
and a negative semantic orientation implies undesirability (e.g., "disturbing",
"superfluous"). This paper introduces a simple algorithm for unsupervised
learning of semantic orientation from extremely large corpora. The method
involves issuing queries to a Web search engine and using pointwise mutual
information to analyse the results. The algorithm is empirically evaluated
using a training corpus of approximately one hundred billion words -- the
subset of the Web that is indexed by the chosen search engine. Tested with
3,596 words (1,614 positive and 1,982 negative), the algorithm attains an
accuracy of 80%. The 3,596 test words include adjectives, adverbs, nouns, and
verbs. The accuracy is comparable with the results achieved by Hatzivassiloglou
and McKeown (1997), using a complex four-stage supervised learning algorithm
that is restricted to determining the semantic orientation of adjectives.

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The categories of this paper are: alg-geom
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The categories of this paper are: nlin
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The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn
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The categories of this paper are: quant-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: econ