Neutrosophic Sentiment Analysis in Transcriptions of in-depth Interviews for Action Research

In-depth interviews are a method widely used by researchers, which are processed once transcribed. Currently, with the development of computer programming, the use of a natural language processing method to analyze such interviews has spread. There are dissimilar programs of various kinds, where lexicons offer highly useful benefits for researchers as they save time and increase efficiency. In essence, these expose how positive or negative a text related to a topic can be. Taking into consideration the usefulness of Neutrosophy for social phenomena and the treatment of indeterminacies, vagueness, and neutralities, it is convenient to establish as an objective of this paper: to develop a method for the sentiment analysis of the transcripts of in-depth interviews in Action Research based on natural language processing and Single Value Neutrosophic Numbers (SVNN). The most important characteristics to take into account in the in-depth interviews will be determined. Then the lexicon will be selected for the natural language processing of the research to fulfill its main objective. Keywords: in-depth interviews, action research, SVNN, VADER.