Reda Khalaf and Mireille Makary, Lebanese International University, Lebanon

Recent studies showed a huge interest in social networks sentiment analysis such as Twitter, to study how the users feel about a certain topic. In this paper, we conducted a sentiment analysis study for the tweets in spoken Lebanese Arabic related to the Lebanon Uprising hash tag (ينتفض_لبنان ,(#which was trending upon a socio-economic revolution that started in October, using different machine learning algorithms. The dataset was manually labelled to measure the precision and recall metrics and to compare between the different algorithms. Furthermore, the work completed in this paper provides two more contributions. The first is related to building a Lebanese – Modern Standard Arabic (فصحة (mapping dictionary and the second is an attempt to detect sarcastic and funny emotions in the tweets using emojis. The results we obtained seem satisfactory especially considering that there was no previous or similar work done involving Lebanese Arabic tweets, to our knowledge.

Lebanese Arabic tweets, sentiment analysis, machine learning, emotions, emojis