Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Subjective Sentiment Analysis for Arabic Newswire Comments   This paper presents an approach based on supervised machine learning methods
to discriminate between positive, negative and neutral Arabic reviews in online
newswire. The corpus is labeled for subjectivity and sentiment analysis (SSA)
at the sentence-level. The model uses both count and TF-IDF representations and
apply six machine learning algorithms; Multinomial Naive Bayes, Support Vector
Machines (SVM), Random Forest, Logistic Regression, Multi-layer perceptron and
k-nearest neighbors using uni-grams, bi-grams features. With the goal of
extracting users sentiment from written text. Experimental results showed that
n-gram features could substantially improve performance; and showed that the
Multinomial Naive Bayes approach is the most accurate in predicting topic
polarity. Best results were achieved using count vectors trained by combination
of word-based uni-grams and bi-grams with an overall accuracy of 85.57% over
two classes and 65.64% over three classes.

34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con