Detecting Sarcasm in Multimodal Social Platforms

Sarcasm is a peculiar form of sentiment expression, where the surface sentiment differs from the implied sentiment. The detection of sarcasm in social media platforms has been applied in the past mainly to textual utterances where lexical indicators (such as interjections and intensifiers), linguistic markers, and contextual information (such as user profiles, or past conversations) were used to detect the sarcastic tone. However, modern social media platforms allow to create multimodal messages where audiovisual content is integrated with the text, making the analysis of a mode in isolation partial. In our work, we first study the relationship between the textual and visual aspects in multimodal posts from three major social media platforms, i.e., Instagram, Tumblr and Twitter, and we run a crowdsourcing task to quantify the extent to which images are perceived as necessary by human annotators. Moreover, we propose two different computational frameworks to detect sarcasm that integrate the textual and visual modalities. The first approach exploits visual semantics trained on an external dataset, and concatenates the semantics features with state-of-the-art textual features. The second method adapts a visual neural network initialized with parameters trained on ImageNet to multimodal sarcastic posts. Results show the positive effect of combining modalities for the detection of sarcasm across platforms and methods.Comment: 10 pages, 3 figures, final version published in the Proceedings of ACM Multimedia 201

Challenges of Sarcasm Detection for Social Network : A Literature Review

Nowadays, sarcasm recognition and detection simplified with various domains knowledge, among others, computer science, social science, psychology, mathematics, and many more. This article aims to explain trends in sentiment analysis especially sarcasm detection in the last ten years and its direction in the future. We review journals with the title’s keyword “sarcasm” and published from the year 2008 until 2018. The articles were classified based on the most frequently discussed topics among others: the dataset, pre-processing, annotations, approaches, features, context, and methods used. The significant increase in the number of articles on “sarcasm” in recent years indicates that research in this area still has enormous opportunities. The research about “sarcasm” also became very interesting because only a few researchers offer solutions for unstructured language. Some hybrid approaches using classification and feature extraction are used to identify the sarcasm sentence using deep learning models. This article will provide a further explanation of the most widely used algorithms for sarcasm detection with object social media. At the end of this article also shown that the critical aspect of research on sarcasm sentence that could be done in the future is dataset usage with various languages that cover unstructured data problem with contextual information will effectively detect sarcasm sentence and will improve the existing performance

Modelos de grafos para la detección de datos de texto no estructurados como el sarcasmo

Sarcasm is frequently characterized as verbal incongruity to communicate scorn. It is a nuanced type of language with which people express something contrary to what is suggested. Perhaps the greatest test in building frameworks to consequently recognize unstructured information, for example, mockery, is the absence of huge, commented on informational indexes. We propose a diagram-based procedure in building conservative language models for sarcasm recognition. This strategy is likewise intended to utilize little information, it could help in different regions like disdain discourse, counterfeit news, and so forth. This charting strategy permits specialists to explore different parts of NLP without obtaining a huge dataset. These days, it still remains a challenge to unmistakably distinguish human slants and feelings by utilizing AI. Associations can use a superior philosophy to settle on proactive choices in basic circumstances. A definite investigation of our examination would hoist the current content mining applications and may help understand better the effect of mockery from the customers and partners communicated in a web-based media climate. We exhibit that straightforward classifiers worked from the model can recognize mockery very well, which they sum up 5 % better than those of the cutting edge.El sarcasmo se define a menudo como una ironía verbal para expresar desprecio, un lenguaje matizado con el que los individuos expresan lo contrario de lo que está implícito. Uno de los mayores retos en la construcción de sistemas para detectar los datos no estructurados como el sarcasmo, es la falta de grandes conjuntos de datos anotados. Proponemos un método basado en grafos para la construcción de modelos de lenguaje compacto para la detección del sarcasmo. Este método está diseñado para usar pocos datos, y podría ayudar a detectar fake news, hate speech, etc.  Permite además a los investigadores analizar otros aspectos del NLP sin tener que obtener un conjunto de datos gigante. Hoy en día, sigue siendo un desafío identificar claramente los sentimientos y emociones humanos mediante el uso de Inteligencia Artificial. Una exploración detallada de nuestra investigación elevaría las aplicaciones actuales de minería de textos y podría ayudar a comprender mejor el impacto del sarcasmo de los clientes y las partes interesadas, expresado en un entorno de redes sociales. Demostramos que los clasificadores simples construidos a partir del modelo pueden detectar bastante bien el sarcasmo, que generalizan un 5% mejor que los del estado del arte

We usually don't like going to the dentist : using common sense to detect irony on Twitter

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