Subjective Sentiment Analysis for Arabic Newswire Comments
Sadik Bessou, Rania Aberkane
Journal of Digital Information Management, Vol. 17, No. 5, 2019.
DOI: https://doi.org/10.6025/jdim/2019/17/5/289-295
Link: http://dline.info/fpaper/jdim/v17i5/jdimv17i5_3.pdf
Abstract:
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 NaÃƒÂ¯ve
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-gramsand bi-grams with an overall accuracy of 85.57%
over two classes and 65.64% over three classes.