  Sentiment analysis is a highly subjective and challenging task. Its
complexity further increases when applied to the Arabic language, mainly
because of the large variety of dialects that are unstandardized and widely
used in the Web, especially in social media. While many datasets have been
released to train sentiment classifiers in Arabic, most of these datasets
contain shallow annotation, only marking the sentiment of the text unit, as a
word, a sentence or a document. In this paper, we present the Arabic Sentiment
Twitter Dataset for the Levantine dialect (ArSenTD-LEV). Based on findings from
analyzing tweets from the Levant region, we created a dataset of 4,000 tweets
with the following annotations: the overall sentiment of the tweet, the target
to which the sentiment was expressed, how the sentiment was expressed, and the
topic of the tweet. Results confirm the importance of these annotations at
improving the performance of a baseline sentiment classifier. They also confirm
the gap of training in a certain domain, and testing in another domain.
