Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to (...) distinguish between can sarcasm none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

The following articke studies the definitions of sarcasm, its usage in literature, in educational system, and its pros and cons.

This article aims to conceptualize the interplay between sarcasm, scatology and writing in Darija (Moroccan Arabic or Al-Maghribia) on the web in a post-uprising era. It focuses on the new Darijophone prose that emerged after 20 February 2011 protests in Goud and The Newزحيليكر‎ (The New Bumpkin). Originating with absurdist February 20 movement founder member, Mohammed Sokrat, this writing genre is realist, vulgar, profane, taboo-breaking, and borrows from the toilet space to poke fun at the schizophrenia, herd mentality and (...) populism that attend a modernizing society under a neoliberal regime. A vocabulary of trash, waste, filthy social types and risqué gags has informed a unique online minoritarian prose that is unpopular, yet widely read. This article studies the virtual politics and poetics of dirt and laughter in written Darija. Writing at the interstices of commitment and co-optation, this subcultural mode of knowledge production reveals Moroccan youth's heretofore suppressed yet incessant longing for change in a post-uprising context of disillusionment. (shrink)

Critique of Sarcastic Reason is a philosophical dissertation that combines several different fields in order to pave the way for those studying sarcasm at the neurobiological, communicative and socio-political levels of analysis where sarcasm appears, respectively, through associated brain activity, between two or more individuals with higher level metabeliefs, and as a method by which political, religious and other social ideologies are attacked (i.e., one form of "biting sarcasm"). The academic disciplines involved in Critique of Sarcastic Reason (...) include social cognitive and developmental psychology, neuroscience, critical theory, modern and contemporary philosophy of mind, evolutionary biology, logic, metaphysics and epistemology. Sarcasm is argued to only function at the highest levels of metacognition, and sarcasm occurs within social situations during which there are tendencies for two or more people to form desires and disgusts directly related to beliefs about beliefs. Sarcasm is compared to deception and is argued to be best analyzed as either spontaneous or rehearsed. (shrink)

Wild Wise Weird: The Kingfisher Story Collection offers a delightful and whimsical mix of Vietnamese fables that are both clever and entertaining. The stories remind me of Aesop's Fables, where each tale imparts valuable lessons through the adventures of the Kingfisher in a quirky bird village. Laced with humor, wit, and a touch of sarcasm, these stories are a joy to read. One standout is Sincere Concern, where the Kingfisher embraces the art of winning friends and influencing others—his key (...) strategy being to smile, greet others, and remember their names. Meanwhile, his assistant, tasked with keeping the Kingfisher from making mistakes, must also take on small jobs to ensure the Kingfisher feels valued and respected. The irony is that, despite all of this, the Kingfisher can never seem to remember his assistant's name! (shrink)

Mobsters and others engaged in risky forms of social coordination and coercion often communicate by saying something that is overtly innocuous but transmits another message ‘off record’. In both ordinary conversation and political discourse, insinuation and other forms of indirection, like joking, offer significant protection from liability. However, they do not confer blanket immunity: speakers can be held to account for an ‘off record’ message, if the only reasonable interpreta- tions of their utterance involve a commitment to it. Legal liability (...) for speech in the service of criminal behavior displays a similar profile of significant protection from indirection along with potential liability for reasonable interpretations. Specifically, in both ordinary and legal contexts, liability depends on how a reasonable speaker would expect a reasonable hearer to interpret their utterance in the context of utter- ance, rather than on the actual speaker’s claimed communicative intentions. (shrink)

The inferentialism due to Robert Brandom presents a compelling normative-deontic picture of language and discursive practices, and as such it is well positioned to address phenomena like lying. This short work outlines a simple account of how lying can be conceptualized within that framework. To that end, the basic Brandomian position is extended to include a novel type of status – namely, pseudo-commitments, which are unique in their being non-binding. The traditional definition of lying is then given a status-oriented form, (...) such that lying consists in presenting a pseudo-commitment with the intention for it to be attributed as a standard commitment. Importantly, this can be articulated in our scorekeeping practices, which is demonstrated with three example exchanges – one featuring an undetected lie, one featuring a detected lie, and one featuring a bald-faced lie. Though simple, the account works and invites extension to other similar phenomena, like sarcasm, acting, bullshit, etc. The notion of pseudo-commitment may also find application outside of the inferentialist context. (shrink)

: Natural Language Processing (NLP), a subset of Artificial Intelligence (AI), has emerged as a powerful tool in analyzing human language to detect underlying mental health issues such as depression and suicidal tendencies. By leveraging techniques in machine learning and deep learning, NLP systems can process and interpret textual data from diverse sources like social media, forums, and online journals to identify patterns and linguistic markers associated with mental health conditions. These markers include the use of emotionally charged words, changes (...) in writing style, expressions of hopelessness, isolation, or distress, which are critical indicators of a person's psychological state. The process typically involves data preprocessing, feature extraction using models such as TF-IDF or word embeddings (e.g., Word2Vec, GloVe), and classification using supervised learning techniques or deep learning models like BERT or LSTM. Understanding human emotions is a critical aspect of human-computer interaction, mental health diagnostics, and customer service applications. This project focuses on leveraging Natural Language Processing (NLP) techniques to analyze and interpret emotions expressed in textual data. By utilizing state-of-the-art machine learning models, including deep learning architectures such as recurrent neural networks (RNNs) and transformers, the project aims to develop models capable of detecting and classifying emotional states such as joy, sadness, anger, fear, and surprise from written content. The research investigates various aspects of emotion detection, including sentiment analysis, contextual understanding, and the role of linguistic features in emotion recognition. Additionally, it explores challenges like sarcasm, ambiguity, and cultural differences in emotional expression. The goal is to create an emotionaware system that can improve applications in fields such as automated customer support, mental health monitoring, and social media analysis, enabling more empathetic and responsive interactions between humans and machines. Through this work, we seek to enhance the understanding of emotions in textual communication, providing valuable insights into emotional dynamics and improving human-computer interactions. (shrink)

The overuse of social media, including the growing mistrust of mainstream news outlets has impacted the increasing influence of online disinformation. Social media has been a powerful tool in Philippine elections because of social media algorithms used by political parties to deploy fake news and damage their rivals. Political fake news on social media began last 2016 and became rampant in the 2022 presidential election when the candidates for the presidency were all victims of trolling. Mainstream media debunked fake news (...) but because of political partisanship, the effort has been useless due to different political beliefs and parties. The methodology involves content analysis to systematically examine social media posts related to political disinformation during the 2022 presidential election specifically focused on identifying two reactions, the emotion-driven and reason-based comments. Most of the comments were emotional and contained sarcasm, threats, slander, and mockery. People’s logical thought processes are influenced by their emotions. Users are frequently looking for information to confirm their existing views and reject or ignore information that contradicts them or is biased in favor of one political party over another. Educating the critical analysis key to both students and social media users is one of the advocacies to avoid spreading fake news in the next generations. In conclusion, emotional responses, such as anger and sarcasm, dominated the discourse, overshadowing reasoned discussions. Media mistrust and political partisanship shaped users’ reactions, leading to reject the fact-checked information especially when it conflicted with their political beliefs. (shrink)

This study explores sentiment analysis of Amazon reviews using NLP and deep learning techniques to understand customer opinions. The main objective is to develop an NLP model using TensorFlow to analyze sentiment in customer reviews. By classifying reviews as positive or negative, the study aims to provide insights into customer satisfaction. The process involves collecting Amazon review data, cleaning the text, and applying deep learning methods to train the model. Various NLP techniques, such as tokenization and word embeddings, help the (...) model understand language patterns. The performance of the model is evaluated to ensure accurate sentiment classification. Challenges like handling sarcasm and ambiguous reviews are considered. The study highlights the importance of deep learning in improving sentiment analysis accuracy. The findings can be useful for businesses to enhance customer experience based on feedback. Future work may explore more advanced techniques or combine text with other forms of data for a more detailed analysis. (shrink)

Twitter is one of the biggest social media sites, where users may share their thoughts, ideas, and opinions as well as discuss current events and live tweets. In the subject of opinion mining, creating reliable and effective algorithms for sarcasm detection on Twitter is an intriguing task. Sarcasm is the use of positive language to convey depressing emotions while speaking in opposition to one’s own intentions. Sarcasm is frequently employed on social networking and micro blogging platforms, where (...) users can offend others and find it difficult to express their true feelings. The deep learning technique utilised in the current algorithms to identify these sarcastic tweets has the limitation of not being able to predict continuous variables. A novel deep learning algorithm is proposed to identify both positive and negative terms as well as sarcasm in comments. Deep neural networks are used to classify the comments into positive and negative word categories. Customers’ opinions are mined using sentiment analysis to find and extract information from the text. Sarcastically stated statements from social networking sites can be quickly categorised and recognised by using VADER (Valence Aware Dictionary and Sentiment Reasoner). (shrink)

This study explores sentiment analysis of Amazon reviews using NLP and deep learning techniques to understand customer opinions. The main objective is to develop an NLP model using TensorFlow to analyze sentiment in customer reviews. By classifying reviews as positive or negative, the study aims to provide insights into customer satisfaction. The process involves collecting Amazon review data, cleaning the text, and applying deep learning methods to train the model. Various NLP techniques, such as tokenization and word embeddings, help the (...) model understand language patterns. The performance of the model is evaluated to ensure accurate sentiment classification. Challenges like handling sarcasm and ambiguous reviews are considered. The study highlights the importance of deep learning in improving sentiment analysis accuracy. The findings can be useful for businesses to enhance customer experience based on feedback. Future work may explore more advanced techniques or combine text with other forms of data for a more detailed analysis. (shrink)