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Sarcasm is often used to humorously criticize something or hurt someone's feelings. Humans often have difficulty in recognizing sarcastic comments since we say the opposite of what we really mean. Thus, automatic sarcasm detection in textual data is one of the most challenging tasks in Natural Language Processing (NLP). It has also become a relevant research area due to its importance in the improvement of sentiment analysis. In this work, we explore several deep learning models such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT) to address the task of sarcasm detection. While most research has been conducted using social media data, we evaluate our models using a news headlines dataset. To the best of our knowledge, this is the first study that applies BERT to detect sarcasm in texts that do not come from social media. Experiment results show that the BERT-based approach overcomes the state-of-the-art on this type of dataset. ; This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of "Fostering Young Doctors Research"(NLP4RARE-CM-UC3M), as well as in the line of "Excellence of University Professors"(EPUC3M17), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).

Mirbeau is often presented as a writer with contradictions, characterised by the vehemence of his indignation. While the virulence of her attacks may be a rhetoric specific to the press article (as in the satirical newspaper 'Les grimaces'), it lasts throughout her career, regardless of the forms used or capitalised success. However, Mirbeau's pamphleral writing is changing in its form. First defined as the mark of concealment, the 'grimace' gradually becomes the emblem of the work carried out by the romancier. For Mirbeau, the distortion is not an obstacle to the truth, but a process used to reveal it, or even the signature of a writer claiming the right to contradict himself. Self-loyalty cannot therefore be based on a doctrine, nor can it really go through discourse. The novel thus offers Mirbeau a shape paradoxically more in line with his conception of truthfulness. Mystification becomes the means of denouncing the impostures of consistency, as does sarcasm, which literally puts the fable of the narrative into a piece. ; Mirbeau est souvent présenté comme un écrivain plein de contradictions, caractérisé par la véhémence de ses indignations. Si la virulence de ses attaques peut relever d'ne rhétorique propre à l'article de presse (comme dans le journal satirique "Les Grimaces"), elle dure tout au long de sa carrière, indépendamment des formes utilisées ou du succès capitalisé. L'écriture pamphlétaire de Mirbeau évolue néanmoins dans ses modalités. D'abord définie comme la marque d'une dissimulation, la « grimace » devient progressivement l'emblème du travail opéré par le romancier. Pour Mirbeau, la distorsion n'est pas un obstacle à la vérité, mais un processus utilisé pour sa révélation, ou même la signature d'un écrivain revendiquant le droit de se contredire. La fidélité à soi-même ne peut donc pas reposer sur une doctrine, ni vraiment passer par un discours. Le roman offre ainsi à Mirbeau une forme sans doute paradoxalement plus conforme à sa conception de la sincérité. La mystification devient le moyen ...

In public political discourse, figurative expressions used by one participant are often followed up and 'countered' by other participants through ironical and/or sarcastic allusions or quotations, which are aimed at denouncing the preceding version and/or deriving a new, contrarian conclusion from it. What is the relationship between the figurative template expression and its ironical or sarcastic variants? Using data from a corpus documenting 25 years of debate in Britain about the nation's place at the heart of Europe, this paper investigates the interplay of metaphor, irony and sarcasm in public discourse. We show that the 'discourse career' of this metaphorical slogan bifurcates into two strands, i.e. an affirmative, optimistic use vs deriding and ridiculing uses that depict the heart of Europe as diseased, dead, non-existent or rotten. It is argued that discourse participants need to retain the optimistic template version as a reference point in discourse memory to achieve the intended ironical and/or sarcastic effects, and that the latter are essential to keep the metaphoricity of the slogan 'alive'.

S'inspirant de Simmel, l'auteur voit dans le sarcasme l'une des formes de la sociabilité‐un type de processus social‐que I'on peut soumettre à l'analyse sociologique. Sa discussion préliminaire traite de la forme systématique, cette géométrie simmélienne du monde social, de la fonction et des corrélatifs structuraux de l'activité sociale. Le sarcasme se manifeste surtout dans les relations directes; il résulte plutôt de la définition sociale d'une situation que de l'existence d'un type singulier de communication dont il constituerait une qualité inhérente. Du point de vue formel, le sarcasme est pergu comme une relation tripartite comprenant la personne qui l'emploie, l'objet de la raillerie et l'auditoire. Dans la pratique, l'objet du sarcasme est souvent l'auditoire et moins souvent l'initiateur lui‐même, ce qui reduit à deux les parties. Le sarcasme a la fonction d'un mécanisme de contrôle, d'un échange qui maintient les distances et sauvegarde les llgnes de démarcation, d'un emploi stratégique de la communication et d'une facon de maitriser et de réduire les tensions. Le statut des participants et 1'intimité du groupe sont les corrélatifs structuraux de l'usage du sarcasme. L'auteur suggere de plus qu'une théorie du sarcasme s'allie intimement au schéme conceptuel de Scheler sur le sentiment.

Sarcasm is one of the problem that affect the result of sentiment analysis. According to Maynard and Greenwood (2014), performance of sentiment analysis can be improved when sarcasm also identified. Some research used Naïve Bayes and Random Forest method on sentiment analysis process. On Salles, dkk (2018) research, in some cases Random Forest outperform the performance by Support Vector Machine that known as a superior method. In this research, we did sentiment analysis on comment section on Instagram account of Indonesian politician. This research compare the accuracy of sentiment analysis with sarcasm detection and analysis sentiment without sarcasm detection, sentiment analysis with Naïve Bayes and Random Forest method then Random Forest for sarcasm detection. This research resulted in accuracy value in sentiment analysis without sarcasm detection with Naïve Bayes 61%, with Random Forest method 72%. Accuracy on sentiment analysis with sarcasm detection using Naïve Bayes – Random Forest method is 60% and using Random Forest – Random Forest method is 71%.

This research is based on the discursive and logometric analysis of the French parliamentary political discourse. The purpose of the article is to note irony and sarcasm as forms of political argument in parliamentary discourse. ; This research is based on the discursive and logometric analysis of the French parliamentary political discourse. The purpose of the article is to note irony and sarcasm as forms of political argument in parliamentary discourse.

Sarcasm is often used to humorously criticize something or hurt someone's feelings. Humans often have difficulty in recognizing sarcastic comments since we say the opposite of what we really mean. Thus, automatic sarcasm detection in textual data is one of the most challenging tasks in Natural Language Processing (NLP). It has also become a relevant research area due to its importance in the improvement of sentiment analysis. In this work, we explore several deep learning models such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT) to address the task of sarcasm detection. While most research has been conducted using social media data, we evaluate our models using a news headlines dataset. To the best of our knowledge, this is the first study that applies BERT to detect sarcasm in texts that do not come from social media. Experiment results show that the BERT-based approach overcomes the state-of-the-art on this type of dataset. ; El sarcasmo se usa con frecuencia para realizar crítica o burla indirecta, a veces hiriendo los sentimientos de alguien. Algunas veces, las personas tienen dificultades para reconocer los comentarios sarcásticos, ya que decimos lo contrario de lo que realmente queremos decir. Por lo tanto, la detección automática de sarcasmo en textos es una de las tareas más complicadas en el Procesamiento del Lenguaje Natural (PLN). Además, se ha convertido en un área de investigación relevante debido a su importancia para mejorar el análisis de sentimientos. En este trabajo, exploramos varios modelos de aprendizaje profundo, como Bidirectional Long Short-Term Memory (BiLSTM) y Bidirectional Encoder Representations from Transformers (BERT) para abordar la tarea de detección de sarcasmo. Si bien la mayoría de los trabajos anteriores se han centrado en datasets construidos con textos de redes sociales, en este artículo, evaluamos nuestros modelos utilizando un dataset formado por titulares de noticias. Por tanto, este es el primer estudio que aplica BERT para detectar el sarcasmo en textos que no provienen de las redes sociales. Los resultados de los experimentos muestran que el enfoque basado en BERT supera el estado del arte en este tipo de conjunto de datos. ; This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of "Fostering Young Doctors Research" (NLP4RARE-CM-UC3M), as well as in the line of "Excellence of University Professors" (EPUC3M17), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).