Evaluating SentiStrength for Arabic Sentiment Analysis

Social networking websites are used today as platforms enabling their users to write down almost anything about everything. Social media users express their opinions and feelings about lots of events occurring in their daily lives. Lots of studies are conducted to study the sentiments presented by social media users regarding different topics. Sentiment Analysis (SA) is a new field that is concerned with measuring the sentiment presented in a given text. Due to their wide set of applications, several SA tools are available. Most of them are designed tor English text. As for other languages such as Arabic, the case is different since only few tools are available. In fact, many of these tools were originally designed for English and were later adapted to deal with Arabic. SentiStrength is an example of tools that are successful tor English and were later adapted to Arabic. However, the adaptation has been done in a crude manner and no deep studies are available to measure the effectiveness of such tools for Arabic text. In this paper, we perform a comprehensive evaluation of SentiStrength using 11 Arabic datasets consisting of tens of thousands of reviews/comments from different domains and in different dialects. We perform the evaluation in terms of positive and negative sentiments. The evaluation results show that overall SentiStrength achieves 62% accuracy, 83.7% precision, 64% recall (positive correct), 68% F1 measure and 55% negative correct.