This study analyzes the reactions of the Italian Twitter community to an environmental demonstration that occurred in Rome on January 2nd, 2023. We compiled a corpus of 368,531 tokens consisting of 11,780 tweets, collected during a 7-day period. We propose a mixed-method approach that combines automated and manual corpus analyses of sentiment, emotions, and implicit language. Our findings offer insights into how tweets reflected the users’ attitudes toward a variety of subjects and entities. Although the sentiment of the overall debate was distributed rather evenly, the incident itself seems to have sparked negative sentiment and emotions among Twitter users. The results of our manual analyses revealed some issues with respect to the automatic classification of sentiment, due to the fact that some tweets contained irony, sarcasm, and slurs. Non-literal interpretations were ignored by the tools at hand that could not account for complex rhetorical argumentative strategies.

Bianco, A., Combei, C.r., Zanchi, C. (2023). Painting the Senate #Green: A Corpus Study of Twitter Sentiment Towards the Italian Environmentalist Blitz. KOMPʹÛTERNAÂ LINGVISTIKA I INTELLEKTUALʹNYE TEHNOLOGII, 22(Supplementary volume), 1021-1031 [10.28995/2075-7182-2023-22-1021-1031].

Painting the Senate #Green: A Corpus Study of Twitter Sentiment Towards the Italian Environmentalist Blitz

Claudia Roberta Combei
;
2023-01-01

Abstract

This study analyzes the reactions of the Italian Twitter community to an environmental demonstration that occurred in Rome on January 2nd, 2023. We compiled a corpus of 368,531 tokens consisting of 11,780 tweets, collected during a 7-day period. We propose a mixed-method approach that combines automated and manual corpus analyses of sentiment, emotions, and implicit language. Our findings offer insights into how tweets reflected the users’ attitudes toward a variety of subjects and entities. Although the sentiment of the overall debate was distributed rather evenly, the incident itself seems to have sparked negative sentiment and emotions among Twitter users. The results of our manual analyses revealed some issues with respect to the automatic classification of sentiment, due to the fact that some tweets contained irony, sarcasm, and slurs. Non-literal interpretations were ignored by the tools at hand that could not account for complex rhetorical argumentative strategies.
2023
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore L-LIN/01
Settore GLOT-01/A - Glottologia e linguistica
English
Twitter discourse
Italian
sentiment analysis
environmental demonstration
https://www.dialog-21.ru/media/6033/dialog2023rinzlr.pdf
Bianco, A., Combei, C.r., Zanchi, C. (2023). Painting the Senate #Green: A Corpus Study of Twitter Sentiment Towards the Italian Environmentalist Blitz. KOMPʹÛTERNAÂ LINGVISTIKA I INTELLEKTUALʹNYE TEHNOLOGII, 22(Supplementary volume), 1021-1031 [10.28995/2075-7182-2023-22-1021-1031].
Bianco, A; Combei, Cr; Zanchi, C
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/410825
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