Delia Irazú Hernández Farías


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INAOE-UPV at SemEval-2018 Task 3: An Ensemble Approach for Irony Detection in Twitter
Delia Irazú Hernández Farías | Fernando Sánchez-Vega | Manuel Montes-y-Gómez | Paolo Rosso
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper describes an ensemble approach to the SemEval-2018 Task 3. The proposed method is composed of two renowned methods in text classification together with a novel approach for capturing ironic content by exploiting a tailored lexicon for irony detection. We experimented with different ensemble settings. The obtained results show that our method has a good performance for detecting the presence of ironic content in Twitter.

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ValenTO at SemEval-2018 Task 3: Exploring the Role of Affective Content for Detecting Irony in English Tweets
Delia Irazú Hernández Farías | Viviana Patti | Paolo Rosso
Proceedings of The 12th International Workshop on Semantic Evaluation

In this paper we describe the system used by the ValenTO team in the shared task on Irony Detection in English Tweets at SemEval 2018. The system takes as starting point emotIDM, an irony detection model that explores the use of affective features based on a wide range of lexical resources available for English, reflecting different facets of affect. We experimented with different settings, by exploiting different classifiers and features, and participated both to the binary irony detection task and to the task devoted to distinguish among different types of irony. We report on the results obtained by our system both in a constrained setting and unconstrained setting, where we explored the impact of using additional data in the training phase, such as corpora annotated for the presence of irony or sarcasm from the state of the art. Overall, the performance of our system seems to validate the important role that affective information has for identifying ironic content in Twitter.


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Annotating Sentiment and Irony in the Online Italian Political Debate on #labuonascuola
Marco Stranisci | Cristina Bosco | Delia Irazú Hernández Farías | Viviana Patti
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper we present the TWitterBuonaScuola corpus (TW-BS), a novel Italian linguistic resource for Sentiment Analysis, developed with the main aim of analyzing the online debate on the controversial Italian political reform “Buona Scuola” (Good school), aimed at reorganizing the national educational and training systems. We describe the methodologies applied in the collection and annotation of data. The collection has been driven by the detection of the hashtags mainly used by the participants to the debate, while the annotation has been focused on sentiment polarity and irony, but also extended to mark the aspects of the reform that were mainly discussed in the debate. An in-depth study of the disagreement among annotators is included. We describe the collection and annotation stages, and the in-depth analysis of disagreement made with Crowdflower, a crowdsourcing annotation platform.


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ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm
Delia Irazú Hernández Farías | Emilio Sulis | Viviana Patti | Giancarlo Ruffo | Cristina Bosco
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)