Qualitative Content Analysis of The New York Times Coronavirus (2019-n-COV) Articles with Natural Language Processing (NLP) and Leximancer

Abstract This study aimed to analyze coronavirus (2019-nCOV) discourse related articles published on NyTimes.com with NLP methods and Leximancer. The textual data of the research were collected with the Orange3 software text mining tool via the NyTimes.com API, and qualitative content analysis was carried out with Leximancer and NLP methods. The research data were selected as three terms (first-mid-last) depending on the determined date ranges during pandemic. The textual data set was analyzed with Leximancer using the qualitative content analysis method and conducted sentiment measurements of the published articles. Then, using Leximancer concept maps, explained concepts and their relationships by visualizing them to identify pandemic discourse. Leximancer results, the distance and proximity positions of some themes related to NyTimes.com pandemic discourse were revealed according to their conceptual definitions. In context of findings can be concluded that NyTimes.com coronavirus (2019-nCOV) discourse some of their concepts have changed periodically, and some concepts remain the same. NyTimes.com's pandemic discourse consisted of a certain part of the concepts that guide human behavior and content that may cause anxiety. Sentiment analysis results were supported these findings Another result was that the findings showed us that the contents of the coronavirus articles were support official policies.