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Social media is growing as a communication medium where people can express online their feelings and opinions on a variety of topics in ways they rarely do in person. Detecting sentiments and emotions in text have gained considerable amount of attention in the last few years. The significant role of the Arab region in international politics and in the global economy have led to the investigation of sentiments and emotions in Arabic. This paper describes our system - SEDAT, to detect sentiments and emotions in Arabic tweets. We use word and document embeddings and a set of semantic features and apply CNN-LSTM and a fully connected neural network architectures to obtain performance results that show substantial improvements in Spearman correlation scores over the baseline models.