Sentiment analysis aims at extracting sentiment embedded mainly in text reviews. The prevalence of semantic web
technologies has encouraged users of the web to become authors as well as readers. People write on a wide range of topics. These
writings embed valuable information for organizations and industries. This paper introduces a novel framework for sentiment
detection in Arabic tweets. The heart of this framework is a sentiment lexicon. This lexiconwas built by translating the SentiStrength
English sentiment lexicon into Arabic and afterwards the lexicon was expanded using Arabic thesauri. To assess the viability
of the suggested framework, the authors have collected and manually annotated a set of 4400 Arabic tweets. These tweets were
classified according to their sentiment into positive or negative tweets using the proposed framework. The results reveal that
lexicons are helpful for sentiment detection. The overall results are encouraging and open venues for future research.