Sarcasm Detection Research Articles

A Fusion Attention Mechanism with BI-LSTM-Based Sarcasm Detection for Selecting High-Profit Products

A Fusion Attention Mechanism with BI-LSTM-Based Sarcasm Detection for Selecting High-Profit Products

A Multi-Level Embedding Framework for Decoding Sarcasm Using Context, Emotion, and Sentiment Feature

Sarcasm detection in text poses significant challenges for traditional sentiment analysis, as it often requires an understanding of context, word meanings, and emotional undertones. For example, in the sentence “I totally love working on Christmas holiday”, detecting sarcasm depends on capturing the contrast between affective words and their context. Existing methods often focus on single-embedding levels, such as word-level or affective-level, neglecting the importance of multi-level context. In this paper, we propose SAWE (Sentence, Affect, and Word Embeddings), a framework that combines sentence-level, affect-level, and context-dependent word embeddings to improve sarcasm detection. We use pre-trained transformer models SBERT and RoBERTa, enhanced with a bidirectional GRU and self-attention, alongside SenticNet to extract affective words. The combined embeddings are processed through a CNN and classified using a multilayer perceptron (MLP). SAWE is evaluated on two benchmark datasets, Sarcasm Corpus V2 (SV2) and Self-Annotated Reddit Corpus 2.0 (SARC 2.0), outperforming previous methods, particularly on long texts, with a 4.2% improvement on F1-Score for SV2. Our results emphasize the importance of multi-level embeddings and contextual information in detecting sarcasm, demonstrating a new direction for future research.

Modeling inter-modal incongruous sentiment expressions for multi-modal sarcasm detection

Modeling inter-modal incongruous sentiment expressions for multi-modal sarcasm detection

Hierarchical Attention and Contextual Embedding for Robust Sarcasm Detection

Objective: The study aims to develop a technique for sarcasm detection in social media text to enrich the sentiment analysis. Methods: The methodology used in the present research is as follows: emotion-embedded vectors for capturing the emotional content of the text, dynamic contextual modulation for the adaption of the model to the given context, and Hierarchical attention mechanism for segmentation of the text at the different level of abstraction. Findings: Evaluation with the test set proved that the proposed model achieved accuracy rates 89% higher than the benchmark models. Including these several complicated methods helped achieve greater accuracy and F1 measures, contributing to sarcasm detection efficiency. Novelty: This approach ensures that vital sarcastic alerts are detected, providing a better text analysis and increasing sentiment analysis's accuracy and robustness. Keywords: Sarcasm detection, Sentiment analysis, Natural language processing, Deep learning, Opinion mining

Emoji-Enhanced Sarcasm Detection: A Comparitive Study of Logistic Regression and LSTM Models

Emoji-Enhanced Sarcasm Detection: A Comparitive Study of Logistic Regression and LSTM Models

Dual-level adaptive incongruity-enhanced model for multimodal sarcasm detection

Multimodal sarcasm detection leverages multimodal information, such as image, text, etc. to identify special instances whose superficial emotional expression is contrary to the actual emotion. Existing methods primarily focused on the incongruity between text and image information for sarcasm detection. Existing sarcasm methods in which the tendency of image encoders to encode similar images into similar vectors, and the introduction of noise in graph-level feature extraction due to negative correlations caused by the accumulation of GAT layers and the lack of representations for non-neighboring nodes. To address these limitations, we propose a Dual-Level Adaptive Incongruity-Enhanced Model (DAIE) to extract the incongruity between the text and image at both token and graph levels. At the token level, we bolster token-level contrastive learning with patch-based reconstructed image to capture common and specific features of images, thereby amplifying incongruities between text and images. At the graph level, we introduce adaptive graph contrast learning, coupled with negative pair similarity weights, to refine the feature representation of the model’s textual and visual graph nodes, while also enhancing the information exchange among neighboring nodes. We conduct experiments using a publicly available sarcasm detection dataset. The results demonstrate the effectiveness of our method, outperforming several state-of-the-art approaches by 3.33% and 4.34% on accuracy and F1 score, respectively.

Impact Of Sarcasm Detection on Sentiment Analysis Using Bi-LSTM and FastText

Sentiment analysis categorizes a collection of texts in a document as either positive or negative. However, sometimes it cannot give accurate results due to sarcastic sentences. Sarcasm involves the use of positive language to convey negative meanings, So sarcasm detection is needed for sentiment classification to provide better results. One method that can be used to perform Sentiment classification is Bidirectional Long Short-Term Memory (Bi-LSTM). However, text data cannot be processed by Bi-LSTM, so it requires word embedding to convert text data into vectors. In this study, the word embedding used is FastText because it can learn the form of words by considering subword information. The results showed that sentiment classification with sarcasm detection could improve evaluation results by 0.08 for precision, 0.07 for recall, 0.07 for F1-score, and 0.07 for accuracy. A paired sample t-test was conducted on precision, recall, F1-score, and accuracy to examine whether there is a difference between sentiment classification with and without sarcasm detection. The obtained p-values are 2.84.10-9, 4.63.10-7, and 2.40.10-8, 6.22.10-8, respectively. This indicates a difference between sentiment classification with and without sarcasm detection. Therefore, with a 95% confidence level, it can be concluded that sarcasm detection impacts sentiment classification.

Sarcasm Detection Based on Sentiment Analysis of Audio Corpus Using Deep Learning

With the rapid progress in digitalization, hundreds of services and applications have begun to depend on human-machine communication, primarily voice-based. As technology advances, it has become increasingly imperative to develop systems that can discern sophisticated emotions from humans, like sarcasm. Despite research that has been conducted on the detection and analysis of speech signals to try and understand emotion, the detection of sarcasm, which is often not overtly expressed, is quite far from something that has received the attention it deserves in recent years. It plays a very important role in any utterance, searching to identify the psychology, mood, or even health behind it. Hence, it does give interactions with humans a more human-like flavor while raising the feeling of understanding human emotions. This article tries to identify sarcasm in speech using audio data derived from real-world, spontaneous, monolingual corpora. In this classification process, a deep learning classifier known as CNN-LSTM model is used. Results of the study showed that a robust combination between feature extraction through convolutional layers and sequential learning through LSTM layers can identify intricate speech patterns in sarcasm. Experimented and validated in terms of performance, this model shows the model to be very good at discriminating between sarcastic and no sarcastic speech, thus promoting it for more extensive use in various applications related to sentiment analysis and in human-computer interaction applications. The results show that this system provides far much closer approximations to human interaction styles, but its impact would extend into a wide area from customer services, monitoring mental health, and even AI-driven communication systems. It is a monolingual and spontaneous corpus that will open the gates for further work and development down the line and possibly extend to multilingual and more diversified speech contexts, thus fine-tuning accuracy in practical scenarios of sarcasm detection.

Multimodal dual perception fusion framework for multimodal affective analysis

The misuse of social platforms and the difficulty in regulating post contents have culminated in a surge of negative sentiments, sarcasms, and the rampant spread of fake news. In response, Multimodal sentiment analysis, sarcasm detection and fake news detection based on image and text have attracted considerable attention recently. Due to that these areas share semantic and sentiment features and confront related fusion challenges in deciphering complex human expressions across different modalities, integrating these multimodal classification tasks that share commonalities across different scenarios into a unified framework is expected to simplify research in sentiment analysis, and enhance the effectiveness of classification tasks involving both semantic and sentiment modeling. Therefore, we consider integral components of a broader spectrum of research known as multimodal affective analysis towards semantics and sentiment, and propose a novel multimodal dual perception fusion framework (MDPF). Specifically, MDPF contains three core procedures: (1) Generating bootstrapping language-image Knowledge to enrich origin modality space, and utilizing cross-modal contrastive learning for aligning text and image modalities to understand underlying semantics and interactions. (2) Designing dynamic connective mechanism to adaptively match image-text pairs and jointly employing gaussian-weighted distribution to intensify semantic sequences. (3) Constructing a cross-modal graph to preserve the structured information of both image and text data and share information between modalities, while introducing sentiment knowledge to refine the edge weights of the graph to capture cross-modal sentiment interaction. We evaluate MDPF on three publicly available datasets across three tasks, and the empirical results demonstrate the superiority of our proposed model.

BERT Model Adoption for Sarcasm Detection on Twitter Data

Sarcasm is a term used to criticize someone's feelings. Sometimes, humans are not able to identify sarcastic comments, and they typically express the reverse of what they mean when they make snarky remarks. Therefore, the detection of sarcasm within a text automatically is a difficult task. Its significance in enhancing sentiment analysis has also made it an important study field. In previous studies, different approaches to deep learning (DL) and machine learning (ML) have been explored. However, previous approaches mainly depend on the lexical and linguistic aspects. Therefore, these techniques could not perform well in the context of sentiment accuracy. In this research, an efficient approach for detecting sarcasm is proposed. A Bidirectional Encoder Representation from a Transformer (BERT) is proposed to improve the sentiment accuracy in this research. This research also aims to compare the two models of deep learning, the BERT and LSTM (Long Short-Term Memory) models. This comparative analysis aims to provide a detailed overview of the pros and cons of each approach for the detection of sarcasm. The primary aim of this study is to examine the different existing ML and DL approaches for the identification of sarcasm. Apart from this, the comparison of BERT and LSTM contributes to the ongoing debate about whether models work best for sarcasm detection in social media. In this study, sentiment analysis's accuracy is improved by making better decisions, especially when it concerns Twitter interactions.

A survey on pragmatic processing techniques

Pragmatics, situated in the domains of linguistics and computational linguistics, explores the influence of context on language interpretation, extending beyond the literal meaning of expressions. It constitutes a fundamental element for natural language understanding in machine intelligence. With the advancement of large language models, the research focus in natural language processing has predominantly shifted toward high-level task processing, inadvertently downplaying the importance of foundational pragmatic processing tasks. Nevertheless, pragmatics serves as a crucial medium for unraveling human language cognition. The exploration of pragmatic processing stands as a pivotal facet in realizing linguistic intelligence. This survey encompasses important pragmatic processing techniques for subjective and emotive tasks, such as personality recognition, sarcasm detection, metaphor understanding, aspect extraction, and sentiment polarity detection. It spans theoretical research, the forefront of pragmatic processing techniques, and downstream applications, aiming to highlight the significance of these low-level tasks in advancing natural language understanding and linguistic intelligence.

Multi-modal sarcasm detection using ensemble net model

Multi-modal sarcasm detection using ensemble net model

A BERT-based with fuzzy logic sentimental classifier for sarcasm detection

This study explores the challenging task of sarcasm detection in text, a crucial aspect of sentiment analysis in Natural Language Processing (NLP). Sarcasm, characterized by irony and nuanced language, often complicates the interpretation of emotional tone in text. To address this, the study employs a hybrid model that integrates BERT (Bidirectional Encoder Representations from Transformers) with fuzzy logic. BERT's deep semantic understanding is combined with the sequential processing of LSTM and the flexible decision-making capabilities of fuzzy logic. The model's performance is validated using multiple datasets, demonstrating a significant improvement in accuracy, particularly in detecting subtle and context-dependent sarcasm. This research contributes to the advancement of sentiment analysis, offering a robust framework for handling complex linguistic expressions in various NLP applications.

An advanced learning approach for detecting sarcasm in social media posts: Theory and solutions

AbstractObjectiveUsers of social media platforms such as Facebook, Instagram, and Twitter can view and share their daily life events through text, photographs, or videos. These platforms receive many sarcastic posts daily because there were fewer limits on what could be posted. The presence of multiple languages and media types in a single post makes it harder to identify sarcastic messages on the current platform than on posts written solely in English.MethodsThis study provides both the theory and solutions about sarcastic post detection on social platforms. Hindi–English code‐mixed data were used to train and test the automated models for sarcasm detection. The models in this study were constructed using traditional machine learning, deep neural networks, LSTM (long short‐term memory), CNN (convolutional neural network), and the combinations of BERT (Bidirectional Encoder Representations from Transformers) with LSTM.ResultsThe experimental results confirm that in the Hindi–English code‐mixed data set, the CNN, LSTM, and BERT‐LSTM ensemble perform best for sarcasm detection. The proposed model achieved an accuracy of 96.29 percent and outperformed by 2.29 percent compared to the existing models.ConclusionThe performance of the proposed system strengthens the code‐mixed sarcastic post detection on social platforms. The model will help filter not only English but also Hindi‐English code‐mixed sarcastic posts on social platforms.

Ambiguity and Sarcasm Detection on Social Media Data Using Ensemble Techniques

Ambiguity and Sarcasm Detection on Social Media Data Using Ensemble Techniques

Advanced Sarcasm Detection in Newspaper Headlines: A Comparative Study of Machine Learning Approaches

This study presents a comprehensive analysis of sarcasm detection in newspaper headlines using various machine learning techniques. We explore the effectiveness of logistic regression, Naïve Bayes (multinomial and Gaussian), random forest, and support vector machine (SVM) models in identifying sarcastic content within the concise format of news titles. Our research addresses the unique challenges posed by the brevity and impactful language characteristic of headlines. The study utilizes a curated dataset of sarcastic and non-sarcastic headlines, employing natural language processing techniques for preprocessing and feature extraction. Performance evaluation metrics include accuracy, precision, recall, and F1-score. Results indicate that the random forest model outperforms other approaches, achieving 94% accuracy in sarcasm detection. This research contributes to the growing field of sentiment analysis and offers insights into the nuanced task of decoding sarcasm in condensed textual formats. KEYWORDS: Random Forest, Support Vector Machine (SVM), TF-IDF, Natural Language Processing (NLP), Subtle Sarcasm

Initial exploration into sarcasm and irony through machine translation

In this paper, we investigate sarcasm and irony as seen through a novel perspective of machine translation. We employ various techniques for translation, comparing both manually and automatically translated datasets of irony and sarcasm. We first clarify the definitions of irony and sarcasm and present an exhaustive field review of studies on irony both from purely linguistic as well as computational linguistic perspectives. We also propose a novel evaluation metric for the purpose of evaluating translations of figurative language, with a focus on machine-translated irony and sarcasm. The constructed English and Chinese parallel dataset includes polarized content from tweets as well as forum posts, categorized by irony types. The preferred translation model, mBART-50, is identified through a thorough experimental process. Optimal translation settings and the best-finetuned model for irony are explored, with the most effective model being finetuned on both ironic and non-ironic data. We also experimented which types of irony are best suitable for training in this specific task — short microblogging messages or longer forum posts. Moreover, we compare the capabilities of a well fine-tuned mBART to a prompt-based method using the recently popular ChatGPT model, with the conclusion that the former still outperforms the latter, although ChatGPT without any training can be considered as a “good enough” ad hoc solution in the case of a lack of data for training. Finally, we verify if the translated data – either manually, or with an MT model – can be used as training data in a task of irony detection. We believe that the presented research can be expanded into languages other than the presented here Chinese and English, which together with the ability to detect various categories of irony, could contribute to deepening the understanding of figurative language, especially irony and sarcasm.

Sarcasm Detection on Social Media: Addressing the Issues of Short and Long Texts Using Machine Learning and Deep Learning Approaches

Sarcasm is a linguistic term that conveys a meaning that is different from what is meant to be said, frequently used to mock, taunt, or convey disdain. Sarcasm is a complicated social phenomenon that can be recognized by its tone of voice, exaggeration, or context. Because sarcastic discourse is nuanced, detecting sarcasm in Natural Language Processing (NLP) has become a major difficulty. This paper thoroughly examines the tradition machine learning approaches like SVM, Naive Bayes and Random Forest well as advanced deep learning methods such as RNN, LSTM, and transformer based models, like BERT, that has demonstrated superior performance used for sarcasm detection, and also the paper offers more sophisticated data preprocessing techniques that comprise several stages, each focusing on a different facet of the fragmented and informal character of the social media material. It focuses mainly on short and long texts that may be found in news headlines and social media sites like Facebook, Instagram, X (formerly known as Twitter), and others. The paper also explores various challenges like cultural and linguistic barriers, and increased use of audio and visual sarcastic content on social media. At last paper concludes with possible guidelines for future works including the development of real-time, multilingual systems to examining holistic strategies that can encompass the complexity of sarcasm in multimodal communications. Key Words: sarcasm, NLP, social media, machine learning, deep learning

Multimodal Sarcasm Detection: A Comprehensive Review

Sarcasm, a form of verbal irony, involves using remarks that intentionally convey the opposite of their literal meaning. It serves to criticize or humorously undermine a situation. Verbal and non-verbal cues—such as changes in tone, incongruence across modalities, and word emphasis—often convey sarcasm. As technology advances, more people express their opinions online, necessitating the development of efficient models for detecting speech nature. Machine Learning algorithms, Linguistic Models, Ensemble Learning, and Multi-modal approaches play crucial roles in this endeavor. While most research has focused on unimodal approaches using textual data, the field now embraces multimodal sarcasm detection. This approach integrates diverse data types, including images, text, and audio, to enhance accuracy and automate sarcasm classification. In our review paper, we delve into the captivating world of multimodal sarcasm detection, tracing its evolution from early unimodal methods to the current state of multimodal techniques. We aim to provide a comprehensive overview, shedding light on challenges, methodologies, and prospects. Keywords: Sarcasm Detection, Multimodal Approaches, Machine Learning Algorithms, Linguistic Models, Ensemble Learning

ArSa-Tweets: A novel Arabic sarcasm detection system based on deep learning model

Sarcasm in Sentiment Analysis (SA) is important due to the sense of sarcasm in sentences that differs from their literal meaning. Analysis of Arabic sarcasm still has many challenges like implicit indirect idioms to express the opinion, and lack of Arabic sarcasm corpus. In this paper, we proposed a new detecting model for sarcasm in Arabic tweets called the ArSa-Tweet model. It is based on implementing and developing Deep Learning (DL) models to classify tweets as sarcastic or not. The development of our proposed model consists of adding main improvements by applying robust preprocessing steps before feeding the data to the adapted DL models. The adapted DL models are LSTM, Multi-headed CNN-LSTM-GRU, BERT, AraBert-V01, and AraBert-V02. In addition, we proposed ArSa-data as a golden corpus that consists of Arabic tweets. A comparative process shows that our proposed ArSa-Tweet method has the most impact accuracy rate based on deploying the AraBert-V02 model, which obtains the best performance results in all accuracy metrics when compared with other methods.