A sample survey based method on transforming linguistic terms into fuzzy sets and the application in MADM problems

Sometimes, people are prone to describe objects with natural languages including words and sentences, so it is useful to compute with words. However, it is obvious that words mean different to different people. Hence, linguistic information must be transformed into numerical forms before aggregation. To deal with this problem, we propose a novel transforming method based on sample survey. Firstly, through survey questionnaire, we collect the numerical data corresponding to each word. Then, we preprocess the collected data to remove those invalid or unreasonable points. Meanwhile, fuzzy sets are valid to present uncertainty, and the triangular fuzzy sets are the simplest one, which can capture the similarity and dissimilarity to the same word from different people. Therefore, based on means and deviations of the remaining data, we ultimately encode the linguistic terms into triangular fuzzy sets and establish the codebooks. The feasibility and effectiveness are illustrated through an application in the MADM problem about shopping online recommendation from real life.