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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Brazilian Social Mood: The Political Dimension of Emotion
verfasst von : Leila Weitzel, Flávia Bernardini, Paulo Quaresma, Claudio André Alves, Wladek Zacharski, Luis G. de Figueiredo
Erschienen in: Experimental IR Meets Multilinguality, Multimodality, and Interaction
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Abstract
Brazil faces a major economic and political crisis. Millions of people joined anti-government protests across the country. Social media sites are a way for some people to vent their emotions without feeling self-conscious. Thus, emotion mining on social media can be viewed as effective tool to conduct Presidential approval rating. This research aims to automatically recognize emotion in texts extracted from social media in Brazilian Portuguese (PT-BR). The ultimate goal is knowing how emotions influence a writer of a text in choosing certain words and/or other linguistic elements. In this research, we perform keyword-based approaches using affect lexicon and a Support Vector Machine and Naïve Bayes algorithms.