## 5 Discussion and State-of-the-Art Comparison
They apply sentiment analysis to extract the preferences and group decision-making methods to select the best hotel. The information extracted is represented by using fuzzy preference relation matrices. Also, the consensus measures are applied over the preferences extracted. This paper is centred on solving one specific problem using a specific review database, while our proposed method is prepared to work over social media discussions. Therefore, it is capable of extracting the information in real time from social media and applying the group decision-making process. In [26], the authors use group decision-making techniques to identify the best sentiment analysis technique to apply in a given situation. They use a hierarchical Analytic Hierarchy Process (AHP) approach with an automatised learning method. Therefore, they do not use sentiment analysis for the same purposes as in our paper. Also, they do not provide consensus measures over the opinions found. When searching in the literature, we could not find a single article that applies Z-numbers to sentiment-analysis-related group decision-making processes. There are several articles, such as [41; 42], that apply Z-numbers to represent preferences in group decision-making methods. However, their reasons for its use differ from the ones proposed in our article. For consensus calculation in [41], they employ an aggregation of preferences based on the extended OWA operator. On the contrary, in [42], they employ the Z-number weighted Bonferroni mean (OZWBM). In conclusion, the mentioned articles are the closest ones to the research carried out in this article that we could find in research databases. Therefore, we could not find any research that directly deals with the reliability and neutrality of experts' contributions in group decision-making processes. The goal of this paper, therefore, has been to design novel consensus measures that provide a solution to this issue. All the methods analysed are designed to be applied in different scenarios and cover different needs, making it difficult to make a fair comparison between them. Depending on the context and needs, some will be more suitable than others. For example, when dealing with large amounts of information, the method in [18] may be the most suitable, because it implements tools that allow for the processing of large amounts of information. When detecting different patterns such as neutrality in opinions or the echo chamber effect, the most appropriate methods to use are [39; 40], respectively. If we are interested in carrying out the decision-making process and obtaining measures of subjectivity and reliability in the extraction of comments, our method is the most appropriate, as it includes these measures. With regard to the efficiency of the methods, it should be noted that the method used to represent preferences has a significant impact, since the simpler the representation of preferences, the faster the final calculation. Also, aggregation operations provide faster results. However, these faster methods end up providing less information about the decision-making process, since the more complex the representation of the information, the more details are represented and the more information we obtain at the end of the process. The use of Z-numbers means that the processes that handle them take longer to process the information. Specifically, it takes twice as long, because two values per preference have to be processed instead of one. However, their use allows the level of reliability in the extraction of the information provided to be identified and stored, which can be very useful when studying the reliability of a preference that has been automatically generated from a text. Table 12 shows a comparison of the characteristics of all the models analysed. Finally, in Table 13, the methods analysed in this section are compared based on a series of characteristics. The presented method has some limitations that we will improve in the future.