## 2 Preliminaries
### Sentiment Analysis  
Traditionally, the most common way to reveal emotions to others was through speech and facial expressions. But with the advancement of technology and social media, people express their emotions through text messages and emoticons on social media. Although the advancement of technology allows users of social media platforms to show their emotions through multimedia content, text is still the most common form used for communication. People profess their emotions through their messages on them (e.g., statuses, comments, blogs, microblogs,...) [20]. Sentiment analysis methods [21] are concerned with identifying how feelings are expressed in texts. We can identify all kinds of concrete feelings: anger, surprise, sadness, joy, etc. We can also make a more general analysis. For example, if in a text we look for expressions that indicate positive (favourable) or negative (unfavourable) reactions towards the subject, we can study the positivity and negativity associated with the subject that the author is dealing with. Specifically, sentiment analysis involves the identification of the following:  
* Sentiment expressions. * Expressions polarity and strength. * Topic relation. Polarity refers to the overall tone of the expression, that is, positive, negative or neutral. Thanks to the polarity analysis of the expression, it is possible to know what is the opinion and sentiment that the writer has on the commented topic. Concretely, it is possible to  
Figure 1: Group decision-making problem-solving process. know how the topic make them feel. The strength of the sentiment allows us to know how much importance the writer attaches to the subject matter. Thanks to this analysis, we can extract information from free text. For example, we can analyse the opinion of users of online documents such as web pages, chat rooms and news articles, instead of doing special surveys with questionnaires that make the user have to provide information explicitly. The result of the sentiment analysis process is a confidence value and a label, such as positive, negative, mixed, neutral or unknown [22]. Sentiment analysis methods provide a degree of occurrence of the sentiment being analysed, which allows us to identify what the author's goal or sentiment was when writing the text. In general, sentiment analysis has been investigated mainly at three levels: at document level, at sentence level and at aspect and entity level. Depending on the context and the topic under discussion, we can distinguish between two types of sentences [3]:  
* Descriptive sentences: In this case, the user is describing a certain topic or element. Therefore, we can apply sentiment analysis to identify a sentiment or several sentiments based on what is stated. * Comparative sentences: This type of sentence compares several elements with each other. In this case, sentiment analysis must be able to identify the elements to be compared and look for comparative expressions that relate them. In this way, it is possible to identify which elements the user prefers. Some of the most frequently applied techniques in the area are as follows [23]:  
1. Sentiment classification: This primarily involves categorizing opinions into three key groups: positive, negative or neutral. The result of many methods is typically a value that falls within a specific range. Various scales can be utilised to assess an opinion, such as the interval \([-1,1]\), with \(-1\) representing the highest level of negativity and \(1\) denoting the highest level of positivity. 2. Subjectivity classification: This mainly consists of detecting whether a given sentence is subjective or not. The higher the percentage of positive and negative words used in the text compared to the total number of words, the higher the degree of subjectivity in the text. 3. Summary of opinions: It is especially focused on extracting the main features of a given topic from a set of texts. It also identifies the feelings associated with such texts. 4.