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People's conduct and reactions are driven by their emotions. Online socialmedia is becoming a great instrument for expressing emotions in written form.Paying attention to the context and the entire sentence help us to detectemotion from texts. However, this perspective inhibits us from noticing someemotional words or phrases in the text, particularly when the words express anemotion implicitly rather than explicitly. On the other hand, focusing only onthe words and ignoring the context results in a distorted understanding of thesentence meaning and feeling. In this paper, we propose a framework thatanalyses text at both the sentence and word levels. We name it CEFER (Contextand Emotion embedded Framework for Emotion Recognition). Our four approachfacets are to extracting data by considering the entire sentence and eachindividual word simultaneously, as well as implicit and explicit emotions. Theknowledge gained from these data not only mitigates the impact of flaws in thepreceding approaches but also it strengthens the feature vector. We evaluateseveral feature spaces using BERT family and design the CEFER based on them.CEFER combines the emotional vector of each word, including explicit andimplicit emotions, with the feature vector of each word based on context. CEFERperforms better than the BERT family. The experimental results demonstrate thatidentifying implicit emotions are more challenging than detecting explicitemotions. CEFER, improves the accuracy of implicit emotion recognition.According to the results, CEFER perform 5% better than the BERT family inrecognizing explicit emotions and 3% in implicit.

CEFER: A Four Facets Framework based on Context and Emotion embedded features for Implicit and Explicit Emotion Recognition