Suchergebnisse

The article solves the problem of identifying markers of positive or negative sentiment in the network discourse that is formed in social networks in relation to a particular politician. The theoretical and methodological foundations of the study were the basics of network linguistics, the network approach, Big Data. To conduct an empirical study using the method of continuous sampling for the keyword "Boris Johnson", data from the social network Twitter was uploaded from May 15 to July 15, 2021 through the Twitter API service. The received dataset amounted to 1 million 900 thousand messages which were divided into a dataset of messages with a positive sentiment and a dataset of messages with a negative sentiment. In each dataset, frequently used fragments are identified and subjected to linguistic discursive analysis. As a result of their analysis, markers of the positive and negative sentiment of the online discourse that is emerging in the Internet space in relation to British Prime Minister Boris Johnson have been identified. They reflect public opinion, the level of trust in a politician, the pole of evaluation of his activities. Considering such markers when developing strategies for working with public opinion will allow changes in the image and reputational potential of public figures and organizations both online and offline.