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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Happy parents’ tweets: An exploration of Italian Twitter data using sentiment analysis Mencarini, Letizia; Hernández Farías, Delia Irazú; Lai, Mirko. - 2019 BASE Show details 2 Processing Affect in Social Media: A Comparison of Methods to Distinguish Emotions in Tweets Meo, Rosa; Sulis, Emilio. - 2017 BASE Show details 3 Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not Sulis, Emilio; Hernández Farías, Delia Irazú; Rosso, Paolo. - 2016 BASE Show details 4 Subjective well-being and social media: A semantically annotated twitter corpus on fertility and parenthood Sulis, Emilio; Bosco, Cristina; Patti, Viviana. - : CEUR-WS, 2016. : country:DEU, 2016. : place:Aachen, 2016 BASE Show details 5 Figurative Messages and Affect in Twitter: Differences Between #irony, #sarcasm and #not Patti, Viviana; Hernandez-Farias, Delia Irazu; Sulis, Emilio. - : Elsevier, 2016 BASE Show details 6 ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm Ruffo, Giancarlo; Bosco, Cristina; Patti, Viviana; Sulis, Emilio; Hernandez Farias, Delia Irazu. - : Association for Computational Linguistics, 2015. : country:USA, 2015. : place:Stroudsburg, PA, 2015 Abstract: This paper describes the system used by the ValenTo team in the Task 11, Sentiment Analysis of Figurative Language in Twitter, at SemEval 2015. Our system used a regression model and additional external resources to assign polarity values. A distinctive feature of our approach is that we used not only word-sentiment lexicons providing polarity annotations, but also novel resources for dealing with emotions and psycholinguistic information. These are important aspects to tackle in figurative language such as irony and sarcasm, which were represented in the dataset. The system also exploited novel and standard structural features of tweets. Considering the different kinds of figurative language in the dataset our submission obtained good results in recognizing sentiment polarity in both ironic and sarcastic tweets. Keyword: figurative language; irony; sarcasm; sentiment analysis; Twitter URL: http://hdl.handle.net/2318/1521357 http://www.aclweb.org/anthology/S15-2117 BASE Hide details

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