Overview of the Evalita 2016 SENTIment POLarity Classification Task

Barbieri, Francesco; Basile, Valerio; Croce, Danilo; Nissim, Malvina; Novielli, Nicole; Patti, Viviana

doi:10.4000/books.aaccademia.1992

Cited by 72 publications

(93 citation statements)

References 2 publications

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“…Emotion awareness in software engineering is receiving increasing attention as part of human factors of software engineering 2 . As such, a recent research trend has emerged to study developers' emotions as they are shared and conveyed in the communication channels within collaborative development environments, including issue tracking systems (e.g., Jira) [22] [27], software repository forges (e.g., GitHub) [15][31] [38], and technical Q&A sites (e.g., Stack Overflow) [7].…”

Section: Introductionmentioning

confidence: 99%

A benchmark study on sentiment analysis for software engineering research

Novielli

Girardi

Lanubile

2018

Proceedings of the 15th International Conference on Mining Software Repositories

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A recent research trend has emerged to identify developers' emotions, by applying sentiment analysis to the content of communication traces left in collaborative development environments. Trying to overcome the limitations posed by using off-the-shelf sentiment analysis tools, researchers recently started to develop their own tools for the software engineering domain. In this paper, we report a benchmark study to assess the performance and reliability of three sentiment analysis tools specifically customized for software engineering. Furthermore, we offer a reflection on the open challenges, as they emerge from a qualitative analysis of misclassified texts. 1

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Section: Introductionmentioning

confidence: 99%

A benchmark study on sentiment analysis for software engineering research

Novielli

Girardi

Lanubile

2018

Proceedings of the 15th International Conference on Mining Software Repositories

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“…The French and Italian parts of the annotated corpus have been respectively exploited as datasets for the first irony detection shared tasks DEFT@TALN2017 7 and for the SENTIPOLC@Evalita shared task on irony detection 8 in both 2014 and 2016 editions (Basile et al, 2014;Barbieri et al, 2016). In particular, currently only the first layer of the annotation scheme has been exploited aiming at detecting if a given tweet is ironic or not.…”

Section: Exploiting the Annotated Corpus For Automatic Irony Detectionmentioning

confidence: 99%

Exploring the Impact of Pragmatic Phenomena on Irony Detection in Tweets: A Multilingual Corpus Study

Karoui¹,

Benamara²,

Patti³

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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This paper provides a linguistic and pragmatic analysis of the phenomenon of irony in order to represent how Twitter's users exploit irony devices within their communication strategies for generating textual contents. We aim to measure the impact of a wide-range of pragmatic phenomena in the interpretation of irony, and to investigate how these phenomena interact with contexts local to the tweet. Informed by linguistic theories, we propose for the first time a multi-layered annotation schema for irony and its application to a corpus of French, English and Italian tweets.We detail each layer, explore their interactions, and discuss our results according to a qualitative and quantitative perspective.

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“…Our system is evaluated on the SENTIPOLC official test data composed of 3000 tweets and the values of precision, recall and average F-score are calculated using the evaluation tool provided by the organizers (Barbieri et al, 2016). As we can see from Table 3, official results of our system are promising, although our research in this domain has to be improved.…”

Section: Resultsmentioning

confidence: 99%

Computational rule-based model for Irony Detection in Italian Tweets

Frenda¹

2016

EVALITA. Evaluation of NLP and Speech Tools for Italian

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English. In the domain of Natural Language Processing (NLP), the interest in figurative language is enhanced, especially in the last few years, thanks to the amount of linguistic data provided by web and social networks.Figurative language provides a non-literary sense to the words, thus the utterances require several interpretations disclosing the play of signification. In order to individuate different meaning levels in case of ironic texts detection, it is necessary a computational model appropriated to the complexity of rhetorical artifice. In this paper we describe our rulebased system of irony detection as it has been presented to the SENTIPOLC task of EVALITA 2016, where we ranked third on twelve participants. Italiano

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Overview of the Evalita 2016 SENTIment POLarity Classification Task

Abstract: Pubblicazione resa disponibile nei termini della licenza Creative Commons Attribuzione -Non commerciale -Non opere derivate 4.0

Cited by 72 publications

References 2 publications

A benchmark study on sentiment analysis for software engineering research

A benchmark study on sentiment analysis for software engineering research

Exploring the Impact of Pragmatic Phenomena on Irony Detection in Tweets: A Multilingual Corpus Study

Computational rule-based model for Irony Detection in Italian Tweets

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