Sentiment Analysis for Arabic Reviews using Machine Learning Classification Algorithms

Sentiment Analysis (SA) is an increasingly important field of study, also known as opinion mining, as it has the ability to identify the emotional tone of a source material as being either positive, negative, or neutral. Simply, SA digests natural languages and extracts insights, making it extremely useful to gain an overview of the public opinion in a certain issue, topic, or even a product. With the vast amount of information over social network platforms, shared views, website reviews, and blogs comes the importance of SA. Arabic is a rich language with extremely complex inflectional and derivational morphology making sentiment analysis in Arabic text more challenging. In this paper, we propose a novel approach to enhance the accuracy of Arabic Sentiment Analysis (ASA). In this aspect, nine supervised machine learning algorithms have been implemented for ASA. Three of these classifiers have never been used before in ASA classification, namely, they are Ridge, Gradient Boosting, and Multi-layer Perceptron. The performance of all nine classifiers is tested using our constructed dataset. The dataset contains 6318 reviews written in different forms of Arabic language and prepared manually by gathering hotel reviews from Booking.com website.

[1]  Luis Alfonso Ureña López,et al.  OCA: Opinion corpus for Arabic , 2011, J. Assoc. Inf. Sci. Technol..

[2]  Galit Shmueli,et al.  To Explain or To Predict? , 2010, 1101.0891.

[3]  Muhammad Zubair Asghar,et al.  RIFT: A Rule Induction Framework for Twitter Sentiment Analysis , 2018 .

[4]  Mohamed S. Kamel,et al.  Extracting domain-specific stopwords for text classifiers , 2017, Intell. Data Anal..

[5]  Amer Al-Badarneh,et al.  A comparison study of some Arabic root finding algorithms , 2010, J. Assoc. Inf. Sci. Technol..

[6]  Amer Al-Badarneh,et al.  The impact of indexing approaches on Arabic text classification , 2017, J. Inf. Sci..

[7]  Manasee Godsay.  The Process of Sentiment Analysis: A Study , 2015 .

[8]  Rehab Duwairi,et al.  Arabic Sentiment Analysis Using Supervised Classification , 2014, 2014 International Conference on Future Internet of Things and Cloud.

[9]  Abdelrahman Osman Elfaki,et al.  A Comparative Survey on Arabic Stemming: Approaches and Challenges , 2017 .

[10]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[11]  Rehab M. Duwairi,et al.  Sentiment analysis for dialectical Arabic , 2015, 2015 6th International Conference on Information and Communication Systems (ICICS).

[12]  Fabrício Benevenuto,et al.  Comparing and combining sentiment analysis methods , 2013, COSN '13.

[13]  Bela Gipp,et al.  Research-paper recommender systems: a literature survey , 2015, International Journal on Digital Libraries.

[14]  Walaa Medhat,et al.  Sentiment analysis algorithms and applications: A survey , 2014 .

[15]  Lei Zhang,et al.  A Survey of Opinion Mining and Sentiment Analysis , 2012, Mining Text Data.

[16]  Rehab Duwairi,et al.  A study of the effects of preprocessing strategies on sentiment analysis for Arabic text , 2014, J. Inf. Sci..

[17]  Qiang Ye,et al.  Sentiment classification of online reviews to travel destinations by supervised machine learning approaches , 2009, Expert Syst. Appl..

[18]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[19]  Yasser El-Sonbaty,et al.  Finding Opinion Strength Using Rule-Based Parsing for Arabic Sentiment Analysis , 2013, MICAI.

[20]  Din J. Wasem,et al.  Mining of Massive Datasets , 2014 .

[21]  Malay Kishore Dutta,et al.  Sentiment analysis based on Support Vector Machine and Big Data , 2016, 2016 39th International Conference on Telecommunications and Signal Processing (TSP).

[22]  Mahieddine Djoudi,et al.  SIAAC: Sentiment Polarity Identification on Arabic Algerian Newspaper Comments , 2017 .

[23]  Muhammad Abdul-Mageed,et al.  SAMAR: Subjectivity and sentiment analysis for Arabic social media , 2014, Comput. Speech Lang..

[24]  Raddouane Chiheb,et al.  Sentiment analysis in Arabic: A review of the literature , 2017, Ain Shams Engineering Journal.

[25]  João Miguel da Costa Sousa,et al.  Decision tree search methods in fuzzy modeling and classification , 2007, Int. J. Approx. Reason..

[26]  Ricardo Aler,et al.  Improving the separation of direct and diffuse solar radiation components using machine learning by gradient boosting , 2017 .

[27]  Mustafa Hammad,et al.  Sentiment Analysis for Arabic Reviews in Social Networks Using Machine Learning , 2016 .

[28]  Khaled Shaalan,et al.  Arabic Tweets Sentimental Analysis Using Machine Learning , 2017, IEA/AIE.