Multi-view informed attention-based model for Irony and Satire detection in Spanish variants

Ortega-Bueno, Reynier; Rosso, Paolo; Pagola, José E. Medina

doi:10.1016/j.knosys.2021.107597

Cited by 13 publications

(4 citation statements)

References 129 publications

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“…That said, irony may serve different purposes; it could, for example, be a way to capture readers' attention (Ortega-Bueno et al 2022). The exclusion of some readers means the inclusion of others who may experience a feeling of exclusiveness.…”

Section: Sw Med Nuvarande Takt Kommer VI Inte Att Nå Målet (Le008)mentioning

confidence: 99%

Discourse of influence – participation and opinions in digital and print media

Ström,

Lindgren,

Boström

et al. 2024

Sakprosa

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In democratic societies, exchange of written opinion is fundamental, but since the 1990s, public debate has gradually moved away from print media platforms to digital media platforms. In order to understand how a shift from print to digital media in opinion-oriented writing may have consequences for interaction and participation, it is relevant to investigate interpersonal aspects of opinion-oriented texts situated in digital and print media. The aim of the study is to explore and compare writers’ attitudes and the construction of the writer-reader-relationship in digital media (represented by Twitter) and (traditionally) print media (represented by letters to the editor, LEs). Bakhtinian dialogism and appraisal theory form the theoretical backdrop of the study. Results show that in the LEs, writers tend to take a negative general value position, but in the tweets, the writers’ attitude is more often either positive or negative, making Twitter a more diverse platform. Furthermore, attitudinal meaning is more transparent in the LEs, indicating that tweets are more often specialized, and readers who are not familiar with the context and interactional style of specific writers may be excluded from the communication. Another result is that a shift from print to digital media platforms seems to turn the focus of discussion more towards attitudes and perspectives in general, and less towards writers’ own attitudes. Therefore, digital media opinion exchange can be understood as a meta-discussion of attitudes, while print media rather transmit writers’ personal attitudes.

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Section: Sw Med Nuvarande Takt Kommer VI Inte Att Nå Målet (Le008)mentioning

confidence: 99%

Discourse of influence – participation and opinions in digital and print media

Ström,

Lindgren,

Boström

et al. 2024

Sakprosa

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show abstract

“…For instance, saying "I've been waiting for ages" when referring to a relatively short period of time. The presence of exaggerated statements can serve as a strong indicator of sarcasm [43].…”

Section: Features Of Sarcasm Detectionmentioning

confidence: 99%

“…Here, the positive term "lovely day" is juxtaposed with the negative situation of having a flat tire. Recognizing such contrasting elements is essential for identifying sarcasm [43].…”

Section: Features Of Sarcasm Detectionmentioning

confidence: 99%

Systematic review of sentiment analysis and predict sarcastic

Kzar,

Safi

2023

J. Al-Qadisiyah Comp. Sci. Math.

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With the rise of social media on the web, sentiment analysis has become one of the most important areas of study. Today, millions of people share their thoughts, ideas, feelings, and opinions on social media sites like Twitter and Facebook. Sentiment analysis, also called opinion mining, is mostly about classifying and predicting how people feel about a certain target. It involves putting text documents or sentences into groups based on how positive or negative they are about a certain topic. Researchers always find "natural language processing" to be one of the most interesting topics. To solve different problems and improve the accuracy of different applications, it is always helpful to know the exact meaning of what is being said in a conversation. Sentiment analysis uses natural language processing (NLP) and learning models like machine learning and deep learning algorithms to figure out how people feel about the data given. Sentiment analysis looks at sarcasm because sarcasm is a way for people to say how they feel about something without saying it directly. People means the exact opposite of what the sentence says at first glance. Sarcasm is hard to figure out because each sarcastic sentence is different. This paper will talk about what has been done in the field of sarcasm detection, the different techniques used, and the problems that still need to be solved.

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“…A detailed summary of these studies can be found in the survey compiled by Joshi, Bhattacharyya, and Carman (2017). Several work explored sarcasm in other languages such as Hindi (Bharti, Sathya Babu, and Jena 2017), Arabic (Abu Farha and Magdy 2020), Spanish (Ortega-Bueno et al 2019), Italian (Cignarella et al 2018), or even code-mixed (Swami et al 2018) languages. Sarcasm and Dialogues.…”

Section: Related Workmentioning

confidence: 99%

Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues

Kumar

Mondal

Akhtar

et al. 2023

AAAI

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Conversations emerge as the primary media for exchanging ideas and conceptions. From the listener’s perspective, identifying various affective qualities, such as sarcasm, humour, and emotions, is paramount for comprehending the true connotation of the emitted utterance. However, one of the major hurdles faced in learning these affect dimensions is the presence of figurative language, viz. irony, metaphor, or sarcasm. We hypothesize that any detection system constituting the exhaustive and explicit presentation of the emitted utterance would improve the overall comprehension of the dialogue. To this end, we explore the task of Sarcasm Explanation in Dialogues, which aims to unfold the hidden irony behind sarcastic utterances. We propose MOSES, a deep neural network which takes a multimodal (sarcastic) dialogue instance as an input and generates a natural language sentence as its explanation. Subsequently, we leverage the generated explanation for various natural language understanding tasks in a conversational dialogue setup, such as sarcasm detection, humour identification, and emotion recognition. Our evaluation shows that MOSES outperforms the state-of-the-art system for SED by an average of ∼2% on different evaluation metrics, such as ROUGE, BLEU, and METEOR. Further, we observe that leveraging the generated explanation advances three downstream tasks for affect classification – an average improvement of ~14% F1-score in the sarcasm detection task and ∼2% in the humour identification and emotion recognition task. We also perform extensive analyses to assess the quality of the results.

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Multi-view informed attention-based model for Irony and Satire detection in Spanish variants

Cited by 13 publications

References 129 publications

Discourse of influence – participation and opinions in digital and print media

Discourse of influence – participation and opinions in digital and print media

Systematic review of sentiment analysis and predict sarcastic

Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues

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