## 2 Related Works  
In this work, we expand the conference paper published in Brain Informatics 2021: _[PERSON], [PERSON], [PERSON]: Emojis Pictogram Classification for Semantic Recognition of Emotional Context_. _BI 2021: 146-156_. Ref. [4] to investigate and benchmark the application of affective classification to emoji pictures using conventional supervised machine learning approaches and deep learning techniques. The previous work is left as a reference for deepening theoretical methods and techniques. The novelty of this work resides in the application of classical machine learning and recent advances in deep learning for image recognition to the new domain of emotions in emojis. Deep learning has previously been investigated for emojic classification with different aims in Natural Language processing, such as sentiment analysis or translation of offensive sexual meanings but never before, to our knowledge, for emotion recognition of emojis in the context of text accessibility and understandability [5]. Our method does not differ from image classification, but applies and compares the known techniques for image classification to emoji classification from text, where the challenge is to recognise emotions from emojis, in order to provide a valid tool for accessibility. From the conventional machine learning techniques, we exploit k-nearest neighbors (K-NN) classifiers [6], Support Vector Machine (SVM) [7], TreeBagger [8], Decision Tree [9], Boosting algorithms [10], Random Forest [8], and Linear Discriminant Analysis (LDA) [11]. In deep learning designs, a significant volume of training samples are required. To address this challenge, we employed transfer learning methods that rely on previous general training on images, able to identify the main elements (e.g., lines, edges, color distribution, shapes) [12; 13]. As deep learning shows improved results when a sufficient amount of training items is provided, this study also exploits deep learning pre-trained classification models for AlexNet [12; 14], GoogleNet [15], SqueezeNet [16], MobileNetV2 [17] and InceptionV3 [18]. We selected these networks due to the availability of a pre-trained version on which to exploit transfer learning. Our observational findings indicate that deep-learning classifiers with transfer learning perform satisfactorily compared to conventional machine learning classifiers on a restricted number of samples, and balanced classes. The majority of research conducted on emotion recognition, based on genuine facial expressions and speech, use a restricted set of emotions [19; 20; 21; 22]. The most widely used and simplest for universal emotion recognition is the Ekman model of six basic emotion classes (i.e., fear, anger, joy, sadness, disgust, and surprise) [23]. One of the main values offered by this model, and the reason it is the most widely used, is that it has been studied worldwide, and proven to be cross-cultural since its facial features are recognized with the same expression without any geographic bias. Furthermore, including a relatively small set of classes, it can guarantee sufficient inter-class variability for classification. The main drawbacks of the [PERSON] model are two-fold: on one hand it guarantees sufficient inter-class variability, on the other hand, it is prone to some error biases versus some specific classes. For instance, a neutral expression can easily be misclassified as sad, but these types of errors also occur in human-based recognition and are thus intrinsic in any dataset. More complex models such as [PERSON]'s [20] may better represent facial emotion expressions since it accounts also for the valence of each emotion, which results in weighted and additional information for data analysis. In addition to these considerations, the [PERSON] model of six basic emotions has provided the inspiration for most of the features behind emotional tagging of text (e.g., view Facebook reactions or emojis sets from any chat-based software), and is well-known for the movie \"Inside out\".