decision-making processes. Among other things, new automatic tools need to be developed to extract information from the networks, analyse it and enable the system to understand the course of the debate. One of the major stumbling blocks in this process is that the information represented in these media uses natural language, which means that special methods are needed for its analysis.  
Current decision-making methods that extract information from networks work with sentiment analysis methods. In this way, by analysing experts' contributions to the debate, it is possible to determine the degree of an expert's preference for an alternative. However, no measure of control over how that result has been extracted and no information other than the calculated preference is established. This can be a problem, as it is impossible to know whether the preference extracted is real or whether the text being analysed has been misinterpreted. Therefore, establishing measures of error or reliability of the information with which these types of automatic preference extraction methods work is very important if we want to be able to trust the results they provide. Within a group decision-making process, it is important that the group of experts obtain a high degree of consensus prior to the application of the selection process [4]. In order to obtain a high level of consensus among the experts, it is advisable to provide the whole group of experts with some advice (feedback) on how far the group is from consensus, what are the most controversial issues (alternatives), what are the major disagreements with the rest of the group and how their change could influence the overall degree of consensus [5].  
In this article, we present a new group decision-making method that works on debates held on social networks. Our objective is to create a new group decision-making method that, based on a debate carried out in a social network, extracts the information from the debate, analyses it, obtains the preferences of the experts on the alternatives and, subsequently, determines the final decision reached. In addition, our method will be able to calculate the consensus reached among the participants. The extraction of preferences is done by using sentiment analysis techniques. This type of technique works on written texts using unstructured natural language. We will perform a dichotomous analysis of the texts (positive sentiments and negative sentiments), of the texts associated with each alternative mentioned by the expert. For the calculation of consensus we propose two new measures of consensus. One is based on the degree of subjectivity of the natural language expressions provided by the experts in the debate. The other measure will compare the preference values extracted for each expert and each alternative. Our method will also include the measurement of opinion reliability. This is calculated as the ratio of positive and negative words used in the discussion. For instance, if an expert uses both positive and negative words in the description of an alternative, then the preference extracted is not as clear as if only positive or negative words were used. In order to take this measure into consideration, we will use Z-numbers [6] to represent the preference values, as this type of representation allows us to represent the reliability of a piece of data.  
The article is organised as follows. Section 2 details the basic concepts necessary to understand the method presented. In Section 3, the proposed method is presented. In Section 4, an applied example of the proposal is proposed using a real example in the social network X. In Section 5, we make a comparison of our method with similar methods already published. We will end by showing some conclusions in Section 6.