Yellow jacket (gilet jaune CHA): an analysis throughout Python dictionaries and media theory
Keywords:Dictionaries, NLP, Twitter, yellow vest
This paper presents an analysis of the French social movement of the gilet jaune (yellow vests, YV) in three parts. The first part focuses on observing and identifying the importance of web content, influencers and users. From the observation of discontent (colère, anger at French), representative words of the movement were identified and stored in an arrangement (array_1). The second part takes the information generated in the first part to perform an analysis of the contents of the social network Twitter through natural language processing (NLP) to identify new adjectives or highlight concepts already observed in array_1 for the creation of a dictionary. The third and last part builds the array_2, which contains words resulting from an enumerative search of adjectives on the news website FranceInfo. In this work, a graphic mapping was generated on the number of times and correlations in which a word is linked with other words that describe a YV movement. For this purpose, the content proposed in array_1 and the content of array_2 were considered in their entirety. Both the NLP dictionaries and the two arrays match in a high percentage with the initial words, which implies that these three independent procedures allow us to compare the results and interpret the public's anger at the YV movement and its possible implications.