## 1 Introduction
### Related Work  
In the last decade, many researchers have proposed SA and ED methods to analyze comments posted on social networks to detect prevailing emotions. In [16], the authors propose a SA method that analyzes customer reviews of tourist attractions by making a 5-star classification of user satisfaction; this method incorporates a lexicon-based process to classify customer reviews of hotels using an existing domain corpus. The study in [17] evaluated user satisfaction scores for political party candidates using an SA algorithm that uses NLP algorithms to collect phrases in tweets linked to -political discourse and to calculate the frequency of words associated with the issues discussed by political participants. In [18] an SA emotion classification method is proposed whereby a semantic text segmentation method is applied to capture sentiments in emails. It involves a data augmentation process in which synthetically labelled data are generated and added to the training set to reduce the probability of overfitting. To refine the classification of documents, some multi-label classification approaches based on deep learning algorithms have been recently proposed. In [19], a hybrid text multi-label classification method is constructed by combining a deep learning algorithm and a back-propagation neural network. In order to extend the size of the training data, a multi-label text classification based on a contrastive learning method is tested in [20] to classify disaster data extracted from social streams. The critical point of the multi-label text classification methods is the computational slowness in the semantic extraction and neural network construction processes. Recently some authors proposed aspect-based sentiment analysis methods (ABSA) [21] to extract emotions from a text with respect to a specific aspect of the product/service analyzed. A survey on the ABSA methods is provided in [22]. Various deep learning algorithms have been used recently in many ABSA frameworks to optimize feature extraction in text [23]. An ABSA framework based on a recurrent neural network is tested in [24]; the authors showed that their framework provides better accuracy than other ABSA methods. In [25], a cascade-target-oriented neural ABSA model is applied in multi-label classifications of user reviews extracted from tweets. The limitation of neural-based ABSA methods is domain dependency; as consumers' comments are strongly linked to product-service, the application of these methods to different domains does not guarantee the same performance. Categorical ED text classification methods have been proposed by some researchers to detect emotions and to classify documents based on categorical emotion models. Comparison between supervised and unsupervised ED text classification methods shows that supervised ED methods provide better performance than unsupervised methods [26]. In [27; 28], the naive Bayes and support vector machine classification algorithms were used to classify tweets using the six Elkmann-based emotion categories. In [29], emotion classification frameworks based on the Elkman models for annotating and classifying tweets using NLP techniques to detect emotional words in a text are proposed. Other authors apply [PERSON]'s wheel of emotions to annotate and classify sentences based on the prevailing detected emotion category. In [30], a panel of annotators evaluated which of the emotional categories of [PERSON]'s wheel was the most relevant in each sentence of a training set of documents; then, bi-directional long-short-term memory and a convolutional neural network were used to classify the documents. In [31], a convolutional neural network text classification method was applied to classify tweets based on the eight emotion categories in [PERSON]'s model. The training set was constructed by collecting tweets with hashtags given by a base emotion word or similar words and classifying them with the corresponding emotional category.