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

Karoui, Jihen; Benamara, Farah; Patti, Viviana; Bosco, Cristina; Aussenac-Gilles, Nathalie

doi:10.18653/v1/e17-1025

Cited by 36 publications

(40 citation statements)

References 27 publications

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“…Our monolingual results are comparable to state of the art for the three languages. The CNN architecture trained on cross-lingual word representation shows that irony has a certain similarity between the languages we targeted despite the cultural differences which confirm that irony is a universal phenomena, as already shown in previous linguistic studies [35,24,9]. The manual analysis of the common misclassified tweets across the languages in the multilingual setup, shows that classification errors are due to three main factors.…”

Section: Discussionsupporting

confidence: 83%

Irony Detection in a Multilingual Context

Ghanem

Karoui²,

Benamara

et al. 2020

Lecture Notes in Computer Science

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This paper proposes the first multilingual (French, English and Arabic) and multicultural (Indo-European languages vs. less culturally close languages) irony detection system. We employ both featurebased models and neural architectures using monolingual word representation. We compare the performance of these systems with state-of-theart systems to identify their capabilities. We show that these monolingual models trained separately on different languages using multilingual word representation or text-based features can open the door to irony detection in languages that lack of annotated data for irony.

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Section: Discussionsupporting

confidence: 83%

Irony Detection in a Multilingual Context

Ghanem

Karoui²,

Benamara

et al. 2020

Lecture Notes in Computer Science

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“…ironic vs. not-ironic) or flag irony as part of sentiment annotations. By contrast, Karoui et al (2017) defined explicit and implicit irony activations based on incongruity in ironic tweets and defined eight fine-grained categories of pragmatic devices that realise such an incongruity, including analogy, hyperbole, rhetorical question, oxymoron, etc. While the typology provides valuable insights into the linguistic realisation of irony, the inter-annotator agreement study demonstrated the complexity of identifying such pragmatic devices, hence it is not clear to which extent such the distinction would be computationally feasible.…”

Section: Data Collection and Annotationmentioning

confidence: 99%

Exploring the fine-grained analysis and automatic detection of irony on Twitter

Hee

Lefever

Hoste

2018

Lang Resources & Evaluation

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To push the state of the art in text mining applications, research in natural language processing has increasingly been investigating automatic irony detection, but manually annotated irony corpora are scarce. We present the construction of a manually annotated irony corpus based on a fine-grained annotation scheme that allows for identification of different types of irony. We conduct a series of binary classification experiments for automatic irony recognition using a support vector machine (SVM) that exploits a varied feature set and compare this method to a deep learning approach that is based on an LSTM network and (pre-trained) word embeddings. Evaluation on a held-out corpus shows that the SVM model outperforms the neural network approach and benefits from combining lexical, semantic and syntactic information sources. A qualitative analysis of the classification output reveals that the classifier performance may be further enhanced by integrating implicit sentiment information and context-and user-based features.

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“…We exploited data from a set of corpora collected exploiting different approaches: self-tagging or manual annotation or crowd-sourcing 3 . We exploited the corpora developed by (Reyes et al, 2013), (Barbieri et al, 2014), (Mohammad et al, 2015), (Ptáček et al, 2014), (Riloff et al, 2013), (Ghosh et al, 2015), (Karoui et al, 2017), and (Sulis et al, 2016). Besides, we also take advantage of an in-house collection of tweets containing the hashtags #irony and #sarcasm 4 .…”

Section: Task A: Ironic Vs Non-ironicmentioning

confidence: 99%

ValenTO at SemEval-2018 Task 3: Exploring the Role of Affective Content for Detecting Irony in English Tweets

Farías

Patti

Rosso

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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

Cited by 36 publications

References 27 publications

Irony Detection in a Multilingual Context

Irony Detection in a Multilingual Context

Exploring the fine-grained analysis and automatic detection of irony on Twitter

ValenTO at SemEval-2018 Task 3: Exploring the Role of Affective Content for Detecting Irony in English Tweets

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