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%.
Highlights
Measuring the satisfaction and obtaining the feedback from users have always been the concern of companies that offer services or products to make decisions that would improve their business
We investigate the effect of objective words on sentiment classification for colloquial Arabic reviews, Jordanian colloquial reviews
We have proposed a machine learning approach for colloquial Arabic sentiment analysis, Jordanian colloquial reviews
Summary
Measuring the satisfaction and obtaining the feedback from users have always been the concern of companies that offer services or products to make decisions that would improve their business. Been used in literature can be classified into two primary categories: supervised approach and semantic orientation approach [6] The former is known as a corpus-based approach which uses machine learning algorithms to classify the sentiment into binary or multiple classes. The lexicons used to extract different sentiment features that can improve the performance of the learning classifier for colloquial Arabic language. We investigate the effect of objective words on sentiment classification for colloquial Arabic reviews, Jordanian colloquial reviews. We introduce a new approach that incorporates different lexicons into SVM classifier to classify the reviews into either positive or negative class.
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