Abstract
The Likert scale is by far the most popular psychometric tool for collecting data. The ordinal structure and confined style of the Likert scale make it prone to information misinterpretation and loss. Depending on the consumers' moods, replies in the real world are sometimes erratic, imprecise, and ill-defined. Neutrosophy (the study of the implementation of the provisions and indeterminacy) is utilized to accurately portray the answers. This work introduces a neutrosophic-informed, agnostic version of the Likert scale. Clustering users based on their comments is an efficient method of segmenting the population and marketing to them. In this research, we offer a clustering approach for responses received using arbitrary Likert scales. When dealing with real-world events, indeterminate Likert scales are superior in recording replies properly.
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