Abstract
Most social media commentary in the Arabic language space is made using unstructured non-grammatical slang Arabic language, presenting complex challenges for sentiment analysis and opinion extraction of online commentary and micro blogging data in this important domain. This paper provides a comprehensive analysis of the important research works in the field of Arabic sentiment analysis. An in-depth qualitative analysis of the various features of the research works is carried out and a summary of objective findings is presented. We used smoothness analysis to evaluate the percentage error in the performance scores reported in the studies from their linearly-projected values (smoothness) which is an estimate of the influence of the different approaches used by the authors on the performance scores obtained. To solve a bounding issue with the data as it was reported, we modified existing logarithmic smoothing technique and applied it to pre-process the performance scores before the analysis. Our results from the analysis have been reported and interpreted for the various performance parameters: accuracy, precision, recall and F-score.
Highlights
Sentiment analysis is a type of natural language processing (NLP), where NLP or computational linguistics, is the scientific study of human languages from a computational perspective [1]
We present a comprehensive review of recent Arabic sentiment analysis research using a component-by-component approach
As we can see from the above table, modern Standard Arabic (MSA) sources are widely used throughout the studies sampled in this survey
Summary
Sentiment analysis is a type of natural language processing (NLP), where NLP or computational linguistics, is the scientific study of human languages from a computational perspective [1]. Sentiment Analysis (SA) is using natural language processing, statistics, or machine learning methods to extract, identify, or otherwise characterize the sentiment content of a text unit [2]. Sentiment analysis has been referred to as opinion mining (OM) and is concerned with the analysis of human opinion, sentiment, and emotion about specific entities (such as food, products, organizations, etc.) and issues (politics, news, etc.) [3][4][5].
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