3.7.1. Fuzzification

The first step in the used fuzzy logic systems is to recognize the input and output variables. In this process, the crisp input data is converted into a fuzzy set with the membership functions [38]. Input variables of the fuzzy logic system are represented on the fuzzy sets by use of linguistic terms, membership functions and linguistic variables. The linguistic terms and variables are frequently the terms or the complete sentences of used natural language. When we are setting the linguistic variables, we are confident enough that no numerical values are used in the linguistic variables. The two vital points Fuzzy sets and fuzzy membership functions which are needed to be used to obtain the fuzzified values. The conversion of crisp input values into fuzzy values are performed by use of Membership Functions and this method of transformation is known as fuzzification. Every membership function signifies a feature of the linguistic variable being fuzzified. As we take this the membership function approach of linguistic variables in our research, we take "Sentiment Score" and "Customer Loyalty" as an input variables which may "Pos" "Neu" "Neg", and the membership function of linguistic variable "Customer Loyalty" is "Pesudo" and "Latent", "True". We described the fuzzified set by following relation:

$$A = \mu\_1 \mathbf{K}(\mathbf{x}\_1) + \mu\_2 \mathbf{K}(\mathbf{x}\_2) + \dots \ \ + \mu\_n \mathbf{K}(\mathbf{x}\_n) \tag{3}$$

In equation (3), the fuzzy set *K*(*xi*) is called as kernel of fuzzification. To apply this technique, *μA* is constant and *xA* is being converted to a fuzzy set *K*(*xi*). This equation is used in the fuzzification process in which Universe of Discourse and membership function are being applied.

In our paper, we take the sentiment analysis score as an input linguistic variable and customer loyalty as an output linguistic variable as shown in Table 3.



We again define linguistic terms for each input and output linguistic variables. The input linguistic variable is sentiment scores and we assigned mainly three linguistic terms. These linguistic terms are Positive, neutral, negative as shown in Table 4.



The output linguistic variable we taken Customer loyalty (LO) also have three linguistic terms, these linguistic terms are True loyalty, pseudo loyalty and latent loyalty, shown in Table 5.


