Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combining Lexical Features and a Supervised Learning Approach for Arabic
  Sentiment Analysis   The importance of building sentiment analysis tools for Arabic social media
has been recognized during the past couple of years, especially with the rapid
increase in the number of Arabic social media users. One of the main
difficulties in tackling this problem is that text within social media is
mostly colloquial, with many dialects being used within social media platforms.
In this paper, we present a set of features that were integrated with a machine
learning based sentiment analysis model and applied on Egyptian, Saudi,
Levantine, and MSA Arabic social media datasets. Many of the proposed features
were derived through the use of an Arabic Sentiment Lexicon. The model also
presents emoticon based features, as well as input text related features such
as the number of segments within the text, the length of the text, whether the
text ends with a question mark or not, etc. We show that the presented features
have resulted in an increased accuracy across six of the seven datasets we've
experimented with and which are all benchmarked. Since the developed model
out-performs all existing Arabic sentiment analysis systems that have publicly
available datasets, we can state that this model presents state-of-the-art in
Arabic sentiment analysis.

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The categories of this paper are: gr-qc
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The categories of this paper are: eess
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The categories of this paper are: nlin
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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: patt-sol
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The categories of this paper are: nucl-ex
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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: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
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The categories of this paper are: supr-con
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The categories of this paper are: astro-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: econ