## 5 Discussion and State-of-the-Art Comparison  
In this paper, we have presented a new group decision-making method that uses sentiment analysis to automatically extract the system preferences. In order to measure the consensus in the process, we present two different measures. One is based on the extracted preferences and the other is based on the subjectivity that experts show in the discussion. In addition, to measure the reliability of the preferences extracted from the debate, we use \(Z\)-numbers. The main advantages provided by the presented method are the following:  
* Preference extraction method based on sentiment analysis: Preference information is obtained directly from the texts that experts provide to the system during the discussion. This approach has two advantages. On the one hand, experts do not have to provide preferences to the system. On the other hand, the preferences are extracted using all the information provided by the experts during the discussion as a source. When we ask the expert to provide preferences, in the end, we are working with summarised information. However, sentiment analysis methods allow us to extract the information from the discussion so that everything that has been discussed in the discussion is taken into account. * Using \(Z\)-numbers to measure the reliability of the extracted information: Our paper uses \(Z\)-numbers to establish a measure of the reliability of the information provided by the experts in the debate. Thus, if an expert provides both positive and negative expressions about an alternative, then the extracted preference will be unreliable. Conversely, if the expert only uses positive or negative expressions to define an alternative, then the reliability of the extracted preference will be high. * Consensus-taking methods calculated directly from the text of the debate: Analysis of the debate makes it possible to establish measures of expert participation in the debate. Thus, the proposed method proposes consensus measures that take into account the involvement of the experts in the debate. That is, if an expert is very clear in their opinion and provides a high percentage of positive expressions depending on the text analysed, then they can be considered to be very vehement in their opinion. On the contrary, if this percentage is low, then it can be stated that the expert is not very involved or acts neutral. Our method uses this measure of subjectivity in opinion to create a new measure of consensus which, together with the measure of consensus calculated from preferences, gives us a lot of information about how the debate is developing and what the predominant positions are. * Calculation of the ranking of alternatives based on the information from the debate: Our method converts the information provided by the experts into preferences and calculates the ranking by using selection methods over the obtained preferences. Thanks to this process, our method is capable of determining which alternatives are preferred by the experts. As the extraction of preferences is automatic, this method works even in decision environments where there is a high number of experts and alternatives. Therefore, it is ideal for application in discussions conducted om social networks. * Applicable to any field: Our proposed model can extract preferences in any field of application, such as tourism, medicine, politics or any other topic where knowledge of users' opinions and emotions can inform decisions. Although the method was initially designed for use in social networks, there is no reason why it cannot be applied in other types of environments, such as written transcripts of debates, closed forums or other types of decision support systems. When there is a large amount of text provided by experts and these texts contain words that allow the positivity or negativity of each expert's discourse to be established, then the proposed method works adequately.