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Affect analysis has recently attracted a great deal of attention due to the rapid development of online social platforms (i.e., Twitter, Facebook). Affect analysis is a part of a broader area of affective computing that aims to detect and grasp human emotions or affects within a piece of writing. Context awareness is very relevant for identifying human emotions and affects behind a piece of text. Capturing the context of a piece of text is often perceived as a challenge. In addition to the own unique features of tweets (shortness, noisiness, short length, etc.), the Arabic language is characterized by its agglutination and morphological richness. In this paper, we address the problem of Arabic affect detection (multilabel emotion classification) by combining the transformer-based model for Arabic language understanding AraBERT and an attention-based LSTM-BiLSTM deep model. AraBERT generates the contextualized embedding, and the attention-based LSTM-BiLSTM determines the label-emotion of tweets by extracting both past and future contexts considering temporal information flow in both directions. Additionally, the attention mechanism is applied to the output of LSTM-BiLSTM to emphasize different words. Our proposed approach was evaluated using the reference dataset of SemEval-2018 Task 1 (Affect in Tweets). The comprehensive results show that the proposed approach outperforms eight current state-of-the-art and baseline methods, and it achieves significant accuracy (53.82%) compared to 1<sup>st</sup> place in SemEval2018-Task1: (Affect in Tweets) competition. In addition, our proposed model outperforms the best recently reported model in the literature, with an enhancement of 2.62% in accuracy.

Combining Context-Aware Embeddings and an Attentional Deep Learning Model for Arabic Affect Analysis on Twitter