Using objective words in the reviews to improve the colloquial arabic sentiment analysis

TL;DR

This paper improves colloquial Arabic sentiment analysis by incorporating objective words alongside sentimental words, using lexicons and SVM classifiers to enhance accuracy on Jordanian reviews.

Contribution

It introduces a novel approach that leverages both objective and sentimental words with custom lexicons for better sentiment classification in colloquial Arabic.

Findings

Achieved 95.6% accuracy in sentiment classification.

Using objective words improves model performance.

Lexicon-based features enhance sentiment analysis accuracy.

Abstract

One of the main difficulties in sentiment analysis of the Arabic language is the presence of the colloquialism. In this paper, we examine the effect of using objective words in conjunction with sentimental words on sentiment classification for the colloquial Arabic reviews, specifically Jordanian colloquial reviews. The reviews often include both sentimental and objective words, however, the most existing sentiment analysis models ignore the objective words as they are considered useless. In this work, we created two lexicons: the first includes the colloquial sentimental words and compound phrases, while the other contains the objective words associated with values of sentiment tendency based on a particular estimation method. We used these lexicons to extract sentiment features that would be training input to the Support Vector Machines (SVM) to classify the sentiment polarity of the reviews. The reviews dataset have been collected manually from JEERAN website. The results of the experiments show that the proposed approach improves the polarity classification in comparison to two baseline models, with accuracy 95.6%.