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1
Irony Detection in Twitter with Imbalanced Class Distributions
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2
Multilingual Stance Detection in Social Media Political Debates
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3
Happy parents’ tweets: An exploration of Italian Twitter data using sentiment analysis
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4
A survey on author profiling, deception, and irony detection for the Arabic language
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5
A Knowledge-Based Weighted KNN for Detecting Irony in Twitter
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6
A survey on author profiling, deception, and irony detection for the Arabic language
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7
Irony and Sarcasm Detection in Twitter: The Role of Affective Content
Hernández Farias, Delia Irazu. - : Universitat Politècnica de València, 2017
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8
Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not
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9
Irony Detection in Twitter: The Role of Affective Content
Patti, Viviana; Hernandez-Farias, Delia Irazu; Rosso, Paolo. - : Association for Computing Machinery (ACM), 2016
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10
Figurative Messages and Affect in Twitter: Differences Between #irony, #sarcasm and #not
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11
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
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12
Analyzing and annotating for sentiment analysis the socio-political debate on #labuonascuola
Stranisci, Marco; Bosco, Cristina; Patti, Viviana. - : aAcademia University Press, 2015. : country:ITA, 2015. : place:Torino, 2015
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