Subjective Sentiment Analysis for Arabic Newswire Comments

TL;DR

This study develops a supervised machine learning approach to classify Arabic newswire comments into positive, negative, or neutral sentiments, achieving up to 85.57% accuracy with n-gram features.

Contribution

It introduces a sentiment analysis model for Arabic news comments using multiple machine learning algorithms and n-gram features, with Naive Bayes performing best.

Findings

N-gram features improve classification performance.

Naive Bayes achieves highest accuracy among tested algorithms.

Count vectors with uni-grams and bi-grams yield best results.

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 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.