## 1 Introduction  
Thanks to the expansion of the Web and social media, most of the world's population today shares textual and multimedia content to express their opinions and feelings about entities, events, places, or people. The use of sentiment analysis (SA) and emotion detection (ED) algorithms makes it possible to analyze the unstructured flows generated on the Web to recognize the predominance of pleasant or unpleasant emotions and to detect the characterizing ones. The arrival of Web 3.0, in which the Web will evolve into a complex decentralized database based on ontological languages, will allow access to a much wider, more structured and complete set of information related to users' experiences and preferences, while maintaining necessary transparency in compliance with all privacy constraints. Already today the Web lives towards a progressive evolution from dynamic Web 2.0 to Web 2.5 in which there are more and more artificial intelligence applications such as SIRI, Google Assistant, and Alexa, which are able to understand the context; moreover, the opportunity to receive benefits and also economic benefits, determines an increasing involvement of users in interacting with applications. In this context, the SA and ED techniques play an even more significant role, with them being capable of capturing and classifying consumers' sensations and likings in the use of products and services. SA and ED techniques are today applied in various fields to analyze unstructured texts posted on the Web and apply natural language processing (NLP) methods to determine the positive or negative opinion of users. In particular, ED methods analyze the text to detect and identify the presence and predominance of different types of feelings and emotions using a specific model of partitioning the types of emotions. Dimensional emotions models define emotions by two or three independent dimensions. In the [PERSON] model [1], all emotions are controlled by two independent dimensions: hedonic (pleasure-displeasure) and arousal (rest-activated). In [2], the emotional state of a person is determined by three parameters: valence, arousal, and power, representing polarity, excitation intensity, and the capacity for restriction on emotions. Categorical emotion models are used to detect and classify emotions in documents. These models are based on the fact that the emotional state is recognizable through the analysis of a basic set of pleasant and unpleasant emotions. Two emotion models are generally used to classify emotions: the [PERSON] model [3], in which emotions are reduced to six basic emotional categories, and the [PERSON] model [4], which classifies emotions into eight pleasant and unpleasant basic emotion categories and eight secondary emotion categories derived from combinations of two basic emotion categories. NLP methods are generally applied to carry out the morphological, syntactic, and semantic analysis of the text, in order to extract information necessary to detect emotional states. Recently, to ensure the accurate text analysis of documents, highly performing NLP neural network models have been proposed [5; 6] and some authors apply deep learning methods for sentiment classification and sentiment extraction in data streams [7; 8]. However, in some cases neural and deep learning models are unsuitable due to the difficulty of obtaining training data; this happens especially in streaming environments where the data flow varies continuously [9], making it impractical to label and extract data samples and causing the presence of unbalanced classified data over time. In some cases, semi-supervised classification methods can be used to classify unbalanced data points [10]. However, to obtain high classification accuracy, a long training phase is necessary, which, computationally, is very expensive.