Cognitive Discourse Analysis can be up-scaled using Sentiment Analysis

Abstract Language is a window into people's thoughts, attitudes, and worldviews, and analyses such as cognitive discourse analysis (CODA) can help reveal this. However, conducting CODA is often resource intensive, requiring considerable time and person-power. By contrast, natural language processing (NLP) approaches may potentially be able to replicate similar results using sentiment analysis for substantially reduced researcher effort. Here, we use respondent-validated data from Amazon product evaluations and Internet Movie Database (IMDB) movie ratings to contrast CODA against two NLP sentiment analysis tools (TextBlob and VADER). NLP approaches showed strong correlations with user-assigned sentiment scores in both data sets. For example, Textblob and VADER show Pearson correlations of r =0.4383 and 0.5188 respectively with the Amazon data and r = 0.594 and r = 0.4664 respectively with the IMDB data (all p < 0.001), while CODA approaches yielded lower correlations (Amazon: r =0.1357; IMDB: r =0.1708 ; p <0.001). Our results show that when it comes to sentiment analysis accuracy and dependability, NLP techniques routinely beat conventional CODA methodologies and, therefore, should be widely adopted, enabling CODA to be undertaken at scale. To help this, we provide an open-source widget \href{https://gitlab.com/lleleena/sasi/-/blob/main/README.md}{Survey Analysis for Sentiment (SASi)}, which reduces the technical requirements of implementing NLP techniques.