Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Improved Text Sentiment Classification Model Using TF-IDF and Next
  Word Negation   With the rapid growth of Text sentiment analysis, the demand for automatic
classification of electronic documents has increased by leaps and bound. The
paradigm of text classification or text mining has been the subject of many
research works in recent time. In this paper we propose a technique for text
sentiment classification using term frequency- inverse document frequency
(TF-IDF) along with Next Word Negation (NWN). We have also compared the
performances of binary bag of words model, TF-IDF model and TF-IDF with next
word negation (TF-IDF-NWN) model for text classification. Our proposed model is
then applied on three different text mining algorithms and we found the Linear
Support vector machine (LSVM) is the most appropriate to work with our proposed
model. The achieved results show significant increase in accuracy compared to
earlier methods.

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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
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The categories of this paper are: astro-ph
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The categories of this paper are: eess
5
The categories of this paper are: gr-qc
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: adap-org
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: cond-mat
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The categories of this paper are: supr-con