Sarcastic Expressions Research Articles

The Differences in Anger Expressions Between Males and Females Among Qassimi Adolescents in the Context of Football Match: A Social Appraisal Perspective

Most studies that focus on studying male and female behavior have not found any gender differences in the expression of anger. Even the claim that men express anger more frequently than women has only been discussed in a few studies. Some studies have found that women express anger more than men or show no gender differences. In this regard, the researcher aimed to explore the gender differences between males and females from a social appraisal perspective in expressing their anger and what males and females use these anger expressions among Qassimi adolescents in the context of football matches. The researcher hypothesises that men and women differ in their social evaluations of expressing and interpreting anger. The present study's findings are significant in exploring the anger expressions used by females and males and their differences in the context of Saudi football matches among Qassimi adolescents in Saudi Arabia. Results from this study were collected using observation and thematic analysis as a research tool to achieve the overall purpose of this study. The results reveal that males were more likely to use violent, bullying, and idiomatic, sarcastic expressions of anger while watching the Saudi football match, specifically in the scene of their favourite team losing, than females. In addition, both male and female participants expressed the extent to which they were aware of social appraisal and the extent of their influence on their actions and words in expressing anger.

Well, Now We Know! Unveiling Sarcasm: Initiating and Exploring Multimodal Conversations with Reasoning

Sarcasm is a widespread linguistic phenomenon that poses a considerable challenge to explain due to its subjective nature, absence of contextual cues, and rooted personal perspectives. Even though the identification of sarcasm has been extensively studied in dialogue analysis, merely detecting sarcasm falls short of enabling conversational systems to genuinely comprehend the underlying meaning of a conversation and generate fitting responses. It is imperative to not only detect sarcasm but also pinpoint its origination and the rationale behind the sarcastic expressions to capture its authentic essence. In this paper, we delve into the discourse structure of conversations infused with sarcasm and introduce a novel task - Sarcasm Initiation and Reasoning in Conversations (SIRC). Embedded in a multimodal environment and involving a combination of both English and code-mixed interactions, the objective of the task is to discern the trigger or starting point of sarcasm. Additionally, the task involves producing a natural language explanation that rationalizes the satirical dialogues. To this end, we introduce Sarcasm Initiation and Reasoning Dataset (SIRD) to facilitate our task and provide sarcasm initiation annotations and reasoning. We develop a comprehensive model named Sarcasm Initiation and Reasoning Generation (SIRG), which is designed to encompass textual, audio, and visual representations. To achieve this, we introduce a unique shared fusion method that employs cross-attention mechanisms to seamlessly integrate these diverse modalities. Our experimental outcomes, conducted on the SIRC dataset, demonstrate that our proposed framework establishes a new benchmark for both sarcasm initiation and its reasoning generation in the context of multimodal conversations. The code and dataset can be accessed from https://www.iitp.ac.in/∼ai-nlp-ml resources.html#sarcasm-explain and https://github.com/GussailRaat/SIRG-Sarcasm-Initiation-and-Reasoning-Generation.

Sarcasm‐based tweet‐level stress detection

AbstractPsychological stress has evolved as an important health concern across the globe. The vulnerability to stress and the ramifications of it have only worsened during the time of the COVID‐19 pandemic. This necessitates a timely diagnosis of stress before the condition progresses to chronicity. In this context, the popularity of social media like Twitter, where large numbers of users share opinions without any social stigma, has emerged as a major resource of human opinions. This has led to an increased research interest in social media‐based stress detection techniques. However, tweet‐level stress detection techniques in the literature have left a void in leveraging the text information in tweets, especially the presence of sarcastic expressions in the tweet's text content. To this end, a novel method called “Sarcasm‐based Tweet‐Level Stress Detection” (STSD) is proposed in this work with the modification of the logistic loss function to detect tweet‐level stress by availing the information of sarcasm that exists in the tweet‐content. The principle of the STSD model is to minimize the loss for non‐sarcastic tweets while maximising the loss for sarcastic tweets. Furthermore, extensive preprocessing and dimensionality reduction are performed using kernel principal component analysis (kernel PCA) to improve the performance by reducing the dimensions. The experimental results show that the proposed STSD model, when applied along with kernel PCA, records a significant improvement in accuracy by a minimum of 5.25% and a maximum of 9.19% over baseline models. Also, there is an increment in F1‐score by at least 0.085 points and a maximum of 0.164 points when compared to the baseline models.

Sentiment analysis of sarcasm detection in social media

When people talk or write more especially on social media, they tend to sometimes, flip the polarity of their expressions. Oftentimes, this change of mood affects the accuracy of the sentiment analysis. Sarcasm is a sophisticated form of irony widely used in social networks and micro blogging websites and is usually a type of sentiment where person speaks the contradictory of what individual word means, expressing gloomy feelings applying positive words. However, it is hard even for humans to recognize. Hence, sarcastic expressions play a vital role in the outcome of automatic sentiment detection in sentiment analysis. To improve on the performance of automatic detection of sentiments in social media networks, there is need for sarcastic statements to be recognised and classify correctly. Sentiment Analysis is a method for identifying people’s opinion, attitude, sentiment, and emotion towards any specific target such as individuals, events, topics, product, organizations, services etc. This paper proposed an ensemble classification method having base classifiers as Random Forest, K-Nearest Neighbour and Naive Bayes for detecting sarcasm using lexical, pragmatic, linguistic incongruity and context incongruity features. The same ensemble method and Support Vector Machines (SVM) were used to identify the sarcasm types so as to diagnose the mood of the users. We report the results and present a comparative evaluation of the ensemble method and SVM classifiers for sarcasm and sarcasm type detection indicating their suitability for the task. The results obtained from the set of experiments conducted indicate the reliability of our approach.

Sarcasm Detection over Social Media Platforms Using Hybrid Ensemble Model with Fuzzy Logic

A figurative language expression known as sarcasm implies the complete contrast of what is being stated with what is meant, with the latter usually being rather or extremely offensive, meant to offend or humiliate someone. In routine conversations on social media websites, sarcasm is frequently utilized. Sentiment analysis procedures are prone to errors because sarcasm can change a statement’s meaning. Analytic accuracy apprehension has increased as automatic social networking analysis tools have grown. According to preliminary studies, the accuracy of computerized sentiment analysis has been dramatically decreased by sarcastic remarks alone. Sarcastic expressions also affect automatic false news identification and cause false positives. Because sarcastic comments are inherently ambiguous, identifying sarcasm may be difficult. Different individual NLP strategies have been proposed in the past. However, each methodology has text contexts and vicinity restrictions. The methods are unable to manage various kinds of content. This study suggests a unique ensemble approach based on text embedding that includes fuzzy evolutionary logic at the top layer. This approach involves applying fuzzy logic to ensemble embeddings from the Word2Vec, GloVe, and BERT models before making the final classification. The three models’ weights assigned to the probability are used to categorize objects using the fuzzy layer. The suggested model was validated on the following social media datasets: the Headlines dataset, the “Self-Annotated Reddit Corpus” (SARC), and the Twitter app dataset. Accuracies of 90.81%, 85.38%, and 86.80%, respectively, were achieved. The accuracy metrics were more accurate than those of earlier state-of-the-art models.

SARCASTIC EXPRESSION IN THE NEWS OF MULAN LIVE-ACTION MOVIE BY CHINA UNCENSORED YOUTUBE CHANNEL

This study uncovers the types and functions of sarcastic expression used in the news of Disney Mulan Live-Action Movie by China Uncensored YouTube channel. The data used in this study is a transcript of the monologue videos taken from China Uncensored YouTube Channel with the title Mulan Backlash: Why Disney’s China Problem Just Got Worse, which was published on September 15, 2020, and has been watched by 426,000 people until October 30, 2020. This study analyzed the types and functions of the sarcastic expressions using Camp’s theory. The most frequently used sarcastic expression was proportional sarcasm with 45% out of 100%, illocutionary sarcasm 27%, lexical sarcasm 18% and the least used sarcasm is like-prefixed sarcasm, which was only 9%. Metaphor and irony appeared in the sarcastic expression of the speech to insult the Movie. The whole sarcastic expressions were used to change Disney’s goals and criticize social and cultural systems.

Affection Enhanced Relational Graph Attention Network for Sarcasm Detection

Sarcasm detection remains a challenge for numerous Natural Language Processing (NLP) tasks, such as sentiment classification or stance prediction. Existing sarcasm detection studies attempt to capture the subtle semantic incongruity patterns by using contextual information and graph information through Graph Convolutional Networks (GCN). However, direct application of dependence may inevitably introduce noisy information and inferiorly in modeling long-distance or disconnected words in the dependency tree. To better learn the sentiment inconsistencies between terms, we propose an Affection Enhanced Relational Graph Attention network (ARGAT) by jointly considering the affective information and the dependency information. Specifically, we use Relational Graph Attention Networks (RGAT) to integrate relation information guided by a trainable matrix of relation types and synchronously use GCNs to integrate affection information explicitly donated by affective adjacency matrixes. The employment of RGAT contributes to information interaction of structural relevant word pairs with a long distance. With the enhancement of affective information, the proposed model can capture complex forms of sarcastic expressions. Experimental results on six benchmark datasets show that our proposed approach outperforms state-of-the-art sarcasm detection methods. The best-improved results of accuracy and F1 are 4.19% and 4.33%, respectively.

A novel algorithm for sarcasm detection using supervised machine learning approach

<abstract> <p>Sarcasm means the opposite of what you desire to express, particularly to insult a person. Sarcasm detection in social networks SNs such as Twitter is a significant task as it has assisted in studying tweets using NLP. Many existing study-related methods have always focused only on the content-based on features in sarcastic words, leaving out the lexical-based features and context-based features knowledge in isolation. This shows a loss of the semantics of terms in a sarcastic expression. This study proposes an improved model to detect sarcasm from SNs. We used three feature set engineering: context-based on features set, Sarcastic based on features, and lexical based on features. Two Novel Algorithms for an effective model to detect sarcasm are divided into two stages. The first used two algorithms one with preprocessing, and the second algorithm with feature sets. To deal with data from SNs. We applied various supervised machine learning (ML) such as k-nearest neighbor classifier (KNN), na?ve Bayes (NB), support vector machine (SVM), and Random Forest (RF) classifiers with TF-IDF feature extraction representation data. To model evaluation metrics, evaluate sarcasm detection model performance in precision, accuracy, recall, and F1 score by 100%. We achieved higher results in Lexical features with KNN 89.19 % accuracy campers to other classifiers. Combining two feature sets (Sarcastic and Lexical) has shown slight improvement with the same classifier KNN; we achieved 90.00% accuracy. When combining three feature sets (Sarcastic, Lexical, and context), the accuracy is shown slight improvement. Also, the same classifier we achieved is a 90.51% KNN classifier. We perform the model differently to see the effect of three feature sets through the experiment individual, combining two feature sets and gradually combining three feature sets. When combining all features set together, achieve the best accuracy with the KNN classifier.</p> </abstract>

Sarcastic Text Detection Using Keras

Sarcastic text is a type of text that contains a kind of irony, in which negative expressions are expressed as positive by attributing meanings to words that contradict their real meanings during communication. During face-to-face communication, changes in tone of voice, body language, eye contact or word stress make it easier for the other person to detect the sarcastic expression. However, it is challenging to detect sarcastic expressions only through text in machine learning-based systems since human-centred qualities cannot be transferred. Newspapers often use sarcastic expressions in their headlines to attract people's attention. However, many people cannot fully understand whether these expressions are sarcastic or not without reading the content of the text. As a result, they can transmit false information to the people around them through direct communication or social media. In this study, to prevent such misinformation, newspaper headlines with and without sarcasm are tried to be classified on three different GPUs with deep learning methods. As a result, the developed model can successfully detect news headlines containing sarcasm.

Techniques and Quality of Ironic, Cynical, and Sarcastic Expressions in Ahmad Tohari’s Ronggeng Dukuh Paruk Trilogy Indonesian – English Translation

Many ironic, cynical, and sarcastic expressions are found in Ronggeng Dukuh Paruk trilogy. Because of the differences of situational context and cultural background, it is not easy for the target readers to grab the information and mission or messages delivered by Ahmad Tohari in the source text. Some techniques might be chosen by the translator. The accuracy, acceptability or naturalness, and readability of the ironic, cynical, and sarcastic expressions should also be considered. The aims of the study were first to identify the translation techniques of ironic, cynical, and sarcastic expression in Ronggeng Dukuh Paruk trilogy from Indonesian into English, and the second, to explore the translation accuracy, acceptability, and readability of translating ironic, cynical, and sarcastic expression in Ronggeng Dukuh Paruk trilogy from Indonesian into English. The object of this study was Ronggeng Dukuh Paruk trilogy written by Ahmad Tohari translated by Rene T.A. Lysloff. This study employed embedded research design. It combined or integrated qualitative and quantitative data. The data analysis was deductively built from particular to general. The result of the translation techniques analysis shows that the most dominant translation techniques are established equivalent, reduction, and modulation. Based on the analysis, it reveals that the translation quality of ironic expression translation possesses the highest score compared with cynic and sarcastic expressions translation. It can be concluded that translation techniques influence the translation quality. It might be suggested for the future researchers to investigate how translation technique influences the translation quality.

Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach.

Sarcasm is the main reason behind the faulty classification of tweets. It brings a challenge in natural language processing (NLP) as it hampers the method of finding people’s actual sentiment. Various feature engineering techniques are being investigated for the automatic detection of sarcasm. However, most related techniques have always concentrated only on the content-based features in sarcastic expression, leaving the contextual information in isolation. This leads to a loss of the semantics of words in the sarcastic expression. Another drawback is the sparsity of the training data. Due to the word limit of microblog, the feature vector’s values for each sample constructed by BoW produces null features. To address the above-named problems, a Multi-feature Fusion Framework is proposed using two classification stages. The first stage classification is constructed with the lexical feature only, extracted using the BoW technique, and trained using five standard classifiers, including SVM, DT, KNN, LR, and RF, to predict the sarcastic tendency. In stage two, the constructed lexical sarcastic tendency feature is fused with eight other proposed features for modelling a context to obtain a final prediction. The effectiveness of the developed framework is tested with various experimental analysis to obtain classifiers’ performance. The evaluation shows that our constructed classification models based on the developed novel feature fusion obtained results with a precision of 0.947 using a Random Forest classifier. Finally, the obtained results were compared with the results of three baseline approaches. The comparison outcome shows the significance of the proposed framework.

Interpretable Multi-Head Self-Attention Architecture for Sarcasm Detection in Social Media.

With the online presence of more than half the world population, social media plays a very important role in the lives of individuals as well as businesses alike. Social media enables businesses to advertise their products, build brand value, and reach out to their customers. To leverage these social media platforms, it is important for businesses to process customer feedback in the form of posts and tweets. Sentiment analysis is the process of identifying the emotion, either positive, negative or neutral, associated with these social media texts. The presence of sarcasm in texts is the main hindrance in the performance of sentiment analysis. Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant, with an intention to insult or ridicule. Inherent ambiguity in sarcastic expressions make sarcasm detection very difficult. In this work, we focus on detecting sarcasm in textual conversations from various social networking platforms and online media. To this end, we develop an interpretable deep learning model using multi-head self-attention and gated recurrent units. The multi-head self-attention module aids in identifying crucial sarcastic cue-words from the input, and the recurrent units learn long-range dependencies between these cue-words to better classify the input text. We show the effectiveness of our approach by achieving state-of-the-art results on multiple datasets from social networking platforms and online media. Models trained using our proposed approach are easily interpretable and enable identifying sarcastic cues in the input text which contribute to the final classification score. We visualize the learned attention weights on a few sample input texts to showcase the effectiveness and interpretability of our model.

Sarcastic Expressions and the Influence of Social Distance and Relative Power in The TV Series <i>Friends</i>

This research aims to identify and classify the sarcastic expressions found in the TV Series Friends. It applies the theory proposed by Camp (2011) which categorized sarcasm into four classes: Propositional, Lexical, Like-Prefixed, and Illocutionary Sarcasm. Considering sarcasm as a Face Threatening Act (FTA), it also analyzes the influence of relative power and social distance to see how these two social variables influence the delivery of sarcastic expressions. The findings show that Illocutionary Sarcasm is the most frequently used class of sarcasm with 30 utterances (45%) followed by Propositional Sarcasm which consists of 25 utterances (37%), Lexical Sarcasm with 10 utterances (15%) and lastly Like-Prefixed Sarcasm that have 2 utterances (3%). With Illocutionary and Propositional Sarcasm are known to be the most straightforward class of sarcasm, the results also show that these two classes of sarcasm are mainly used when the social distance (D) is negative where the interlocutors does not have the obligation to be polite due to their close intimacy. In cases where the social distance has positive value, the value of power (P) helps to determine which class of sarcasm is used. Furthermore, a clear context and common ground is essential to help identify sarcastic expressions and the intention underlying it.

Sarcastic Expressions in The <i>Simpsons</i> Movie

This research identifies the sarcastic utterances found in The Simpsons Movie. It allocates the utterances said by the characters into four categories proposed by Camp (2011): propositional, lexical, like-prefixed, and illocutionary sarcasm. After that, the categorized utterances are identified based on their illocutionary functions by using Leech’s theory (1983): collaborative, convivial, competitive, and conflictive function. The result shows that there are 20 sarcastic utterances found in the movie, 85% of which belongs to illocutionary sarcasm, and the 15% belongs to propositional sarcasm. However, the lexical sarcasm and like-prefixed sarcasm are not used by the characters in expressing sarcasm. Meanwhile, there are only three types of functions that are found in this research; collaborative, conflictive, and convivial. This research also displays evidence that sarcasm is used to protect the speaker from the unwanted consequences that might come from the hearer’s reaction. In addition to context and common ground, the hearer’s response is also necessary to detect an utterance as sarcastic expression.

Context-Based Feature Technique for Sarcasm Identification in Benchmark Datasets Using Deep Learning and BERT Model

Sarcasm is a complicated linguistic term commonly found in e-commerce and social media sites. Failure to identify sarcastic utterances in Natural Language Processing applications such as sentiment analysis and opinion mining will confuse classification algorithms and generate false results. Several studies on sarcasm detection have utilised different learning algorithms. However, most of these learning models have always focused on the contents of expression only, leaving the contextual information in isolation. As a result, they failed to capture the contextual information in the sarcastic expression. Secondly, many deep learning methods in NLP uses a word embedding learning algorithm as a standard approach for feature vector representation, which ignores the sentiment polarity of the words in the sarcastic expression. This study proposes a context-based feature technique for sarcasm Identification using the deep learning model, BERT model, and conventional machine learning to address the issues mentioned above. Two Twitter and Internet Argument Corpus, version two (IAC-v2) benchmark datasets were utilised for the classification using the three learning models. The first model uses embedding-based representation via deep learning model with bidirectional long short term memory (Bi-LSTM), a variant of Recurrent Neural Network (RNN), by applying Global Vector representation (GloVe) for the construction of word embedding and context learning. The second model is based on Transformer using a pre-trained Bidirectional Encoder representation and Transformer (BERT). In contrast, the third model is based on feature fusion that comprised BERT feature, sentiment related, syntactic, and GloVe embedding feature with conventional machine learning. The effectiveness of this technique is tested with various evaluation experiments. However, the technique's evaluation on two Twitter benchmark datasets attained 98.5% and 98.0% highest precision, respectively. The IAC-v2 dataset, on the other hand, achieved the highest precision of 81.2%, which shows the significance of the proposed technique over the baseline approaches for sarcasm analysis.

SARCASM AND THE TRANSLATION QUALITY IN THE SUBTLE ART OF NOT GIVING A FUCK BOOK

Sarcasm is a harsher style of satire in hurtful jokes with a specific purpose. Sarcasm is the dominant language style used in Mark Manson's The Subtle Art of Not Giving A Fuck. This study aims to identify sentence forms of sarcasm and analyze the translation quality of sarcastic expressions. This translation study employs a qualitative descriptive design. The research data takes the form of sentences containing sarcasm and its translation. The data is collected through document analysis, interviews, and focus group discussions. The results showed four types of sarcasm in the book The Subtle Art of Not Giving A Fuck, including ridicule, satire, proximity, and humor. Besides, the translation quality of the book The Subtle Art of Not Giving A Fuck has a high level of accuracy, acceptability, and readability, evidenced by the proper use of eleven translation techniques applied by the translator to 145 data. The frequent techniques contributing to the quality of translation are compensation, adaptation, transposition, and modulation techniques.

ИСТОКИ ЭМОЦИОНАЛЬНО-ЭКСПРЕССИВНОГО СТИЛЯ В ЖИТИИ ПРОТОПОПА АВВАКУМА

Protopop Avvakum was a religious leader of the Orthodox Schism, an opponent of Patriarch Nikon and his reforms. In his autobiography, he tells about his misadventures and tribulations during his exile in Siberia and Pustozersk. His Life was compared to confession, because of its personal tone. However, the lyricism of the narrative disappears when it comes to enemies: Patriarch Nikon, Tsar Alexei Mikhailovich, and “evil superiors”. Sarcastic expressions and abusive vocabulary convey the state of mind of the author, who is ready to sacrifice his life for the old true faith. The metaphorical and sharp language of the protopop, full of spiritual power, is a major characteristic of his Life, letters and petitions.

ОСОБЛИВОСТІ ЗАСТОСУВАННЯ СЕНТИМЕНТ-АНАЛІЗУ (ІНТЕРПРЕТАЦІЯ САРКАЗМУ, БАГАТОЗНАЧНОСТІ, ЗАПЕРЕЧЕННЯ ТА МУЛЬТИПОЛЯРНОСТІ)

In the past decades, sentiment analysis has become one of the most active research areas in natural language processing, data mining, web mining, and information retrieval. The great demand in everyday life and the factor of novelty coupled with the availability of data from social networks have served as strong motivation for research on sentiment-analysis. A number of technical problems, most of which had not been attempted before, either in the NLP or linguistics communities have also generated strong research interests in academia. Sentiment analysis, also called opin-ion mining, is the field of study that analyzes people’s opinions, sentiments, apprais-als, attitudes, and emotions toward entities and their attributes expressed in written text. The entities can be products, services, organizations, individuals, events, issues, or topics. The field represents a large problem space. It improves not only the field of natural language processing but also management, political science, economics, and sociology because all these areas are related to the thoughts of consumers and public. User-generated content is full of opinions, because the main reason why people post messages on social media platforms is to express their views and opinions, and therefore sentiment analysis is at the centre of social media analysis. It turned out that user messages often contain plenty of sarcastic expressions and ambiguous words. Within one opinion both positive and negative sentiments can be present. This also applies to negative particles, which do not always indicate a negative tone. This article investigates four challenges faced by researchers while conducting sentiment analysis, namely: sarcasm, negation, word ambiguity, and multipolarity. These aspects significantly affect the accuracy of the results when we determine a sentiment. Modern approaches to solving the problem are also covered. These are mainly machine learning methods, such as convolutional neural networks (CNN), deep neural networks (DNN), long short-term memory (LTSM), recurrent neural network (RNN), support vector machines (SVM), etc.

Empirical study of shallow and deep learning models for sarcasm detection using context in benchmark datasets

Sarcastic expressions tend to flip the polarity of posts when being analyzed for sentiments. Detecting sarcastic tone, which conveys a sharp, bitter, or cutting expression, remark or taunt in natural language is tricky even for humans, making its automated detection more arduous. Computational models for sarcasm detection have often relied on the content of utterances in isolation whereas using contextual information definitely improves it. This work is a preliminary to understand the what, how and why of using context in sentiment analysis. The concept of ‘context in use’ is described by exemplifying content-based local and global context to predict sarcasm in user-generated social textual data. In this research Twitter data of benchmark SemEval 2015 Task 11 and nearly 20 k posts from Reddit are classified as sarcastic or non-sarcastic using three predictive learning models. The first model is based on the conventional Term Frequency-Inverse Document Frequency (TF-IDF) weighting which is trained over three classifiers, namely the Multinomial Naive Bayes, Gradient Boosting and Random Forest and Ensemble Voting is utilized to generate the output. In the second model, a combination of semantic (sentiment) and pragmatic (punctuation) features are considered to model the context along with the top-200 TF-IDF features and results are observed using five baseline classifiers (Decision Tree, Support Vector Machine, Random Forest, K-Nearest Neighbour and Multi Layer Perceptron). The final model uses deep learning, that is, the Long–Short-Term-Memory (LSTM) and its variant Bi-directional LSTM applying GloVe (Global Vectors for Word Representation) for building semantic word embeddings and learning context. The empirical study using training set and test set performance metrics (Accuracy, Recall, Precision, F1 Score) is done to compare the three learning models for sarcasm classification within two datasets, and it is observed that Bi-directional LSTM model has the highest accuracy of 86.32% and 82.91% for the Twitter and Reddit datasets respectively.

Discovering the influence of sarcasm in social media responses

AbstractSarcasm in verbal and nonverbal communication is known to attract higher attention and create deeper influence than other negative responses. Many people are adept at including sarcasm in written communication thus sarcastic comments have the potential to stimulate the virality of social media content. Although diverse computational approaches have been used to detect sarcasm in social media, the use of text mining to explore the influential role of sarcasm in spreading negative content is limited. Using tweets during a service disruption of a leading Australian organization as a case study, we explore this phenomenon using a text mining framework with a combination of statistical modeling and natural language processing (NLP) techniques. Our work targets two main outcomes: the quantification of the influence of sarcasm and the exploration of the change in topical relationships in the conversations over time. We found that sarcastic expressions during the service disruption are higher than on regular days and negative sarcastic tweets attract significantly higher social media responses when compared to literal negative expressions. The content analysis showed that consumers initially complaining sarcastically about the outage tended to eventually widen the negative sarcasm in a cascading effect towards the organization's internal issues and strategies. Organizations could utilize such insights to enable proactive decision‐making during crisis situations. Moreover, detailed exploration of these impacts would elevate the current text mining applications, to better understand the impact of sarcasm by stakeholders expressed in a social media environment, which can significantly affect the reputation and goodwill of an organization.This article is categorized under: Technologies > Data Preprocessing Ensemble Methods > Text Mining Application Areas > Industry Specific Applications Fundamental Concepts of Data and Knowledge > Big Data Mining