Blog 21: Politicians, experts, and journalists

11 mars 2022


Research Notebook

'Mixology' is an open research project, which aims to extract opinions in times of crisis, here from a corpus collected via the Twitter API, from December 12 to 31, 2021.

Three social worlds can be considered the main players in the Covid crisis: politics, science, and media. This part of the sentiment analysis brings together all the tweets from the English-speaking corpus, i.e. 465,440 tweets published between December 12 and 31, 2021. The political world is rather unanimous against it, with an average score of 50.25 %. This average score is comparable with regard to its actors (50.62%). It is in France that the score is highest for negative type sentiment, with 57.49%.

Conversely, the scientific world and its actors obtain majority scores with regard to the positive type of sentiment, except in Luxembourg where the lack of representativeness cannot lead to the conclusion that there would be considerable mistrust towards science. As far as the experts are concerned, the scores are comparable from one country to another. Sentiments are more divided with regard to the media (pay attention to the Luxembourg scores which cannot be considered as representative). However, the results are less divided with regard to the figure of the journalist: from 64.49% of positive sentiment in Austria to 36.96% in Switzerland.

Although a sentiment analysis of a corpus of tweets is limited in scope (representativeness/diversity of users, temporality limited in time, research carried out on a determined number of keywords, subjectivity of a subjective evaluation), they provide indications of a general loss of confidence in the political world and its representatives, at least with regard to the management of the Covid crisis.


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Read more

Blog 21: Politicians, experts, and journalists

Blog 20: For vaccination, against restrictions

Blog 19: Comparative Sentiment Analysis

Blog18: A health and political crisis

Blog 17: Anatomy of the “political/sanitary measures” sub-corpus (en)

Blog 16: Sentiment analysis of the ‘vaccination’ sub-corpus (en, part.2)

Blog 15: Comparative sentiment analysis of the ‘vaccination’ sub-corpus (en, part.1)

Blog 14: An adapted dictionary for the Covid crisis and sentiment analysis

Blog 13: Building a stop words list

Blog 12: Main Dictionaries for Sentiment Analysis

Blog 11: Statistical description of the corpus #RStats

Blog 10: Sentiment analysis or the assessment of subjectivity

Blog 9: Topic modeling of the ‘vaccination’ corpus (English)

Blog 8: Linguistic and quantitative processing of the ‘vaccination’ corpus (English, part.2)

Blog 7: Linguistic and quantitative processing of the ‘vaccination’ corpus (English, part.1)

Blog 6: Collecting the corpus and preparing the lexical analysis

Blog 5: The textclean package

Blog 4: Refining the queries

Blog 3: The rtweet package

Blog 2: Collecting the corpus

Blog 1: An open research project

The challenges of research on media use in times of crisis