Abstract
Twitter considered as a rich resource to collect people's opinions in different domains and attracted researchers to develop an automatic Sentiment Analysis (SA) model for tweets. In this work, a semantic Arabic Twitter Sentiment Analysis (ATSA) model is developed based on supervised machine learning (ML) approaches and semantic analysis. Most of the existing Arabic SA approaches represent tweets based on the bag-of-words (BoW) model. The main limitation of this model is that it is semantically weak; where words considered as independent features and ignore the semantic associations between them. As a result, synonymous words that appear in two tweets are represented as different independent features. To overcome this limitation, this work proposes enriching the tweets representation with concepts utilizing Arabic WordNet (AWN) as an external knowledge base. In addition, different concepts representation approaches are developed and evaluated with naive Bayes (NB) and support vector machine (SVM) ML classifiers on an Arabic Twitter dataset. The experimental results indicate that using concepts features improves the performance of the ATSA model compared with the basic BoW representation. The improvement reached 4.48% with the SVM classifier and 5.78% with the NB classifier.