Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Language-Independent Sentiment Analysis Using Subjectivity and
  Positional Information   We describe a novel language-independent approach to the task of determining
the polarity, positive or negative, of the author's opinion on a specific topic
in natural language text. In particular, weights are assigned to attributes,
individual words or word bi-grams, based on their position and on their
likelihood of being subjective. The subjectivity of each attribute is estimated
in a two-step process, where first the probability of being subjective is
calculated for each sentence containing the attribute, and then these
probabilities are used to alter the attribute's weights for polarity
classification. The evaluation results on a standard dataset of movie reviews
shows 89.85% classification accuracy, which rivals the best previously
published results for this dataset for systems that use no additional
linguistic information nor external resources.

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The categories of this paper are: supr-con
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The categories of this paper are: math
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: q-alg
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The categories of this paper are: stat
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg
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The categories of this paper are: cond-mat