Sentiment Analysis on Interview Transcripts: An application of NLP for Quantitative Analysis

One-on-one interviews and manual analysis of their transcripts is the most common way researchers get into depth to obtain detailed insights. These insights are highly subjective and often lack objectivity. In this paper we demonstrate a method and a use case to bring objectivity to this such analyses. We present the use of Natural Language Processing (NLP) to generate sentiment analysis and perform various quantitative techniques. This analysis is useful in deriving insights by finding patterns and building a simple linear model to explain the variation in sentiments pattern. We also present a view advocating the usage of this technique for the effective and optimal time usage by researchers to learn maximum from outlier interviews.