## 5 Discussion and State-of-the-Art Comparison
However, there may be cases where some experts have provided very little information to the system or where the texts they share are veryneutral. In these cases, it is difficult to automatically extract their opinion. The method will assign that expert a neutral opinion value for the alternative, neither positive nor negative. To solve this, it is possible to ask the expert to provide new information to the system and suggest that they express their opinion more directly. The positivity and negativity of opinions is linked to experts using words collected in the dictionaries used for sentiment detection. There may be cases in which the dictionary used does not include the terminology associated with a particular topic or the information does not match to how a particular expert expresses themself. It should be noted that these cases will be minimal, as these dictionaries contain terms that are commonly used by everyone, regardless of the topic being discussed. However, if this occurs, specific dictionaries with the terms used in the topic being discussed or by the expert should be used. Another possible solution is to expand existing dictionaries with new information. Similarly, if we want to work with texts that are not in English or that use a specific tone, it is necessary to include dictionaries that contain the positive, negative and neutral terms for the required cases. In recent state of the art on the topic, there are few papers that include consensus in a sentiment-analysis-related group decision-making process. For example, in [38], the authors present a novel group decision-making method that uses a sentiment analysis approach to extract preferences from texts and, afterwards, apply consensus measures over the results. For representing the information, they employ distributed linguistic preference relations (DLPRs) for representing the information once it is extracted from the texts. A sentiment analysis approach using dictionaries is used for extracting the preferences. On [39], authors study the phenomenon of echo chambers in group decision-making problems by applying sentiment analysis and consensus measures. They extract opinions from social media and calculate a sentiment polarity matrix. By the use of Aspect-Based Sentiment analysis (ABSA), they use a polarity analysis to calculate consensus and detect echo chambers. In [18], authors present a novel group decision-making process that uses sentiment analysis for detecting aggressiveness and positivity in experts' comments. Since the method is centred in dealing with a high amount of information, they use clusters of experts in order to be able to handle it. Although there are methods like the ones mentioned above that use consensus measures and sentiment analysis in group decision-making methods, they do not include any reliability or subjectivity measures. Therefore, they extract less information from the debate and have less means to improve the decision process performance. In [40], authors use sentiment analysis to detect neutral opinions and improve sentiment analysis procedures in order to carry out a better automatic preference extraction process. They employ the Induced Ordered Weighted Averaging (IOWA) operator for aggregating the information. In order to calculate consensus, they employ the proximity function to the neutral point. This paper already worked on detecting neutrality and identifies the concepts of subjectivity and reliability. Nevertheless, the paper is centred on detecting and filtering all those neutrality contributions. In our paper, we developed two novel measures in order to detect and work with those two cases separately. Instead of filtering unreliable texts, we give the experts a chance to modify their way of expressing themselves in order to be clearer. In the case of experts being too neutral, we can recommend to them that they be more involved in the process. In [19], authors resolve the problem of hotel selection by using a hotel database review.