To match it corpus, i obtained from the fresh Politoscope databases 25, 883 tweets authored by the new eleven individuals and you may no other key political leaders between (pick Text message B inside the S1 Document). This next corpus gets the benefit of reflecting the new themes one to emerged inside political arguments, on their own of candidates’ programmatic orientations.
There have been two kinds of traditional techniques for the fresh new extraction of subject areas of unstructured text message: co-phrase study and you will question modeling that have LDA such actions . In these steps, subject areas is defined as “handbags away from words”, inferred regarding the statistics from appearance of a list of predefined statement the brand new data. Which list was alone gotten due to mostly state-of-the-art text message-mining methods in industries of absolute language processing (NLP) and you may servers understanding.
Thus, i reviewed both of these corpora with the CNRS text-exploration application Gargantext ( discover source at that implements advanced NLP actions and you will co-word topic recognition; and additionally visual analytics approaches for new expression and you may correspondence into the overall performance.
In the 1st few procedures, Gargantext uses a variety of lemmatization, post-marking and mathematical analysis instance tf-idf and you may genericity/specificity studies to spot on text-exploration couples thousand sets of terms that will be specific towards political discourse. age. end terms and conditions otherwise badly formed terms who does provides enacted the text-mining methods was basically eliminated, crucial hashtags or neologisms regarding Fb particularly frexit was indeed added). History, we very carefully discover every governmental procedures with the selected words emphasized on text to be sure no very important keywords was lost. Read More