Arabic Sentiment Analysis using WEKA a Hybrid Learning Approach

Data has become the currency of this era and it is continuing to massively increase in size and generation rate. Large data generated out of organisations' e-transactions or individuals through social networks could be of a great value when analysed properly. This research presents an implementation of a sentiment analyser for Twitter's tweets which is one of the biggest public and freely available big data sources. It analyses Arabic, Saudi dialect tweets to extract sentiments toward a specific topic. It used a dataset consisting of 3000 tweets collected from Twitter. The collected tweets were analysed using two machine learning approaches, supervised which is trained with the dataset collected and the proposed hybrid learning which is trained on a single words dictionary. Two algorithms are used, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). The obtained results by the cross validation on the same dataset clearly confirm the superiority of the hybrid learning approach over the supervised approach.