In online interactions, users frequently add emojis (e.g., smileys, hearts, angry faces) to text for expressing the emotions behind the communication context, aiming at a better interpretation to text especially of polysemous short expressions. Emotion recognition refers to the automated process of identifying and classifying human emotions. If text-based emoticons (i.e., emojis created by textual symbols and characters) can be directly understood by semantic-based context recognition tools used in the Web and Artificial Intelligence and robotics, image-based emojis need instead image recognition for a complete semantic context interpretation. This study aims to explore and compare systematically different classification models of emoticon pictograms collected from the Internet, with different labels according to the Ekman model of six basic emotions. A first comparison involves supervised machine learning classifiers trained on features extracted through neural networks. In the second phase, the comparison is extended to different deep learning models. Results indicate that deep learning models performed excellent, and traditional supervised algorithms also achieve very promising outcomes.
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