Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sentiment Analysis of Review Datasets Using Naive Bayes and K-NN
  Classifier   The advent of Web 2.0 has led to an increase in the amount of sentimental
content available in the Web. Such content is often found in social media web
sites in the form of movie or product reviews, user comments, testimonials,
messages in discussion forums etc. Timely discovery of the sentimental or
opinionated web content has a number of advantages, the most important of all
being monetization. Understanding of the sentiments of human masses towards
different entities and products enables better services for contextual
advertisements, recommendation systems and analysis of market trends. The focus
of our project is sentiment focussed web crawling framework to facilitate the
quick discovery of sentimental contents of movie reviews and hotel reviews and
analysis of the same. We use statistical methods to capture elements of
subjective style and the sentence polarity. The paper elaborately discusses two
supervised machine learning algorithms: K-Nearest Neighbour(K-NN) and Naive
Bayes and compares their overall accuracy, precisions as well as recall values.
It was seen that in case of movie reviews Naive Bayes gave far better results
than K-NN but for hotel reviews these algorithms gave lesser, almost same
accuracies.

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The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
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The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
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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: nucl-th
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The categories of this paper are: q-fin
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The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn