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Sarcasm detection has gained significance in sentiment analysis, especially when social media is rife with cyberbullying and trolling. Emojis have garnered researchers’ interest as they are polysemic. But they are usually employed in combination with other modalities in sarcasm detection. This work proposes an emoji-focused approach to study their role in sarcasm detection. This structured approach studies how emojis, sentiment of text and emojis, most frequent emoji in text, and its position helps with sarcasm classification task. Experiments include various machine learning classifiers and BERT fine-tuned for emojis to reveal the decisive role of emojis in sarcasm detection, even in the absence of any other modality. Novel sarcasm-aware GloVe-based emoji embeddings are presented that outperform other available emoji embeddings to achieve highest F1, MCC, and RoC_AuC scores on two unseen datasets. Emoji embeddings, BERT fine-tuned with emojis, and emoji-focused models presented in this work can be used by researchers as baseline for sarcasm classification when situational or conversational context and other modalities like visuals, audio etc. are absent. This emoji-focused approach can be useful in identifying bullying or hateful content on public platforms and even on private chat-based platforms where users may more frequently imply sarcasm under the guise of emojis.

Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, and other aspects derived. Social Opinion Mining can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. The latest developments in Social Opinion Mining beyond 2018 are also presented together with future research directions, with the aim of leaving a wider academic and societal impact in several real-world applications.

Sarcasm detection plays an important role in natural language processing as it can impact the performance of many applications, including sentiment analysis, opinion mining, and stance detection. Despite substantial progress on sarcasm detection, the research results are scattered across datasets and studies. In this paper, we survey the current state-of-the-art and present strong baselines for sarcasm detection based on BERT pre-trained language models. We further improve our BERT models by fine-tuning them on related intermediate tasks before fine-tuning them on our target task. Specifically, relying on the correlation between sarcasm and (implied negative) sentiment and emotions, we explore a transfer learning framework that uses sentiment classification and emotion detection as individual intermediate tasks to infuse knowledge into the target task of sarcasm detection. Experimental results on three datasets that have different characteristics show that the BERT-based models outperform many previous models.

Sarcasm detection research in the Bengali language so far can be considered to be narrow due to the unavailability of resources. In this paper, we introduce a large-scale self-annotated Bengali corpus for sarcasm detection research problem in the Bengali language named ‘Ben-Sarc’ containing 25,636 comments, manually collected from different public Facebook pages and evaluated by external evaluators. Then we present a complete strategy to utilize different models of traditional machine learning, deep learning, and transfer learning to detect sarcasm from text using the Ben-Sarc corpus. Finally, we demonstrate a comparison between the performance of traditional machine learning, deep learning, and transfer learning models on our Ben-Sarc corpus. Transfer learning using Indic-Transformers Bengali Bidirectional Encoder Representations from Transformers as a pre-trained source model has achieved the highest accuracy of 75.05%. The second-highest accuracy is obtained by the long short-term memory model with 72.48% and Multinomial Naive Bayes is acquired the third highest with 72.36% accuracy for deep learning and machine learning, respectively. The Ben-Sarc corpus is made publicly available in the hope of advancing the Bengali Natural Language Processing Community. The Ben-Sarc is available at https://github.com/sanzanalora/Ben-Sarc .

Sarcasm is a modest kind of mockingly expressing one’s own thoughts. With the advent of social networking communication, new routes of sociability have proliferated. It may also be stated that the four chariots of being socially hilarious nowadays are humour, irony, sarcasm, and wit. Sarcasm is a clever means of encapsulating any intrinsic truth, message, or even satire in a humorous way. In this paper, we manually extract the features of a benchmark pop culture sarcasm corpus encompassing sarcastic conversations and monologues in order to build padding sequences from the vector representations’ matrices. We also suggest a hybrid of four Parallel Long Short Term Networks, each with its own activation classifier. Consecutively it achieves 98.31% accuracy among the test cases on open-source English literature. Our approach transcends several previous state-of-the-art works and results in sophisticated sarcastic statement generation. We also culture the probable prospects for producing even better refined automated sarcasm generation.

This work aims to provide a novel multimodal sarcasm detection model that includes four stages: pre-processing, feature extraction, feature level fusion, and classification. The pre-processing uses multimodal data that includes text, video, and audio. Here, text is pre-processed using tokenization and stemming, video is pre-processed during the face detection phase, and audio is pre-processed using the filtering technique. During the feature extraction stage, such text features as TF-IDF, improved bag of visual words, n-gram, and emojis as well on the video features using improved SLBT, and constraint local model (CLM) are extraction. Similarly the audio features like MFCC, chroma, spectral features, and jitter are extracted. Then, the extracted features are transferred to the feature level fusion stage, wherein an improved multilevel canonical correlation analysis (CCA) fusion technique is performed. The classification is performed using a hybrid classifier (HC), e.g. bidirectional gated recurrent unit (Bi-GRU) and LSTM. The outcomes of Bi-GRU and LSTM are averaged to obtain an effective output. To make the detection results more accurate, the weight of LSTM will be optimally tuned by the proposed opposition learning-based aquila optimization (OLAO) model. The MUStARD dataset is a multimodal video corpus used for automated sarcasm discovery studies. Finally, the effectiveness of the proposed approach is proved based on various metrics.

Sarcasm is a form of sentiment whereby people express the implicit information, usually the opposite of the message content in order to hurt someone emotionally or criticise something in a humorous way. Sarcasm identification in textual data, being one of the hardest challenges in natural language processing (NLP), has recently become an interesting research area due to its importance in improving the sentiment analysis of social media data. A few studies have carried out a comprehensive literature review on sarcasm identification in the existing primary study within the last 11 years. Thus, this study carried out a review on the classification techniques for sarcasm identification under the aspects of datasets, pre-processing, feature engineering, classification algorithms, and performance metrics. The study has considered the published article from the period of 2008 to 2019. Forty (40) academic literature were selected from the 7 standard academic databases in order to carry out the review and realize the objectives. The study revealed that most researchers created their own datasets since there is no standard available datasets in the domain of sarcasm identification. Context and content-based linguistic features were used in most of the studies. This review shows that n-gram and parts of speech tagging techniques were the most commonly used feature extraction techniques. However, binary representation and term frequency were utilized for feature representation whereas Chi squared and information gain were used for the feature selection scheme. Moreover, classification algorithm such as support vector machine, Naïve Bayes, random forest, maximum entropy, and decision tree algorithm were mostly applied using accuracy, precision, recall and F-measure for performance measures. Finally, research challenges and future direction are summarized in this review. This review reveals the impact of sarcasm identification in building effective product reviews and would serve as handle resources for researchers and practitioners in sarcasm identification and text classification in general.

Social media platforms such as Twitter and Facebook have become popular channels for people to record and express their feelings, opinions, and feedback in the last decades. With proper extraction techniques such as sentiment analysis, this information is useful in many aspects, including product marketing, behavior analysis, and pandemic management. Sentiment analysis is a technique to analyze people's thoughts, feelings and emotions, and to categorize them into positive, negative, or neutral. There are many ways for someone to express their feelings and emotions. These sentiments are sometimes accompanied by sarcasm, especially when conveying intense emotion. Sarcasm is defined as a positive sentence with underlying negative intention. Most of the current research work treats them as two distinct tasks. To date, most sentiment and sarcasm classification approaches have been treated primarily and standalone as a text categorization problem. In recent years, research work using deep learning algorithms have significantly improved performance for these standalone classifiers. One of the major issues faced by these approaches is that they could not correctly classify sarcastic sentences as negative. With this in mind, we claim that knowing how to spot sarcasm will help sentiment classification and vice versa. Our work has shown that these two tasks are correlated. This paper proposes a multi-task learning-based framework utilizing a deep neural network to model this correlation to improve sentiment analysis's overall performance. The proposed method outperforms the existing methods by a margin of 3%, with an F1-score of 94%.

Recently, sequential transfer learning emerged as a modern technique for applying the “pretrain then fine-tune” paradigm to leverage existing knowledge to improve the performance of various downstream NLP tasks, with no exception of sentiment analysis. Previous pieces of literature mostly focus on reviewing the application of various deep learning models to sentiment analysis. However, supervised deep learning methods are known to be data hungry, but insufficient training data in practice may cause the application to be impractical. To this end, sequential transfer learning provided a solution to alleviate the training bottleneck issues of data scarcity and facilitate sentiment analysis application. This study aims to discuss the background of sequential transfer learning, review the evolution of pretrained models, extend the literature with the application of sequential transfer learning to different sentiment analysis tasks (aspect-based sentiment analysis, multimodal sentiment analysis, sarcasm detection, cross-domain sentiment classification, multilingual sentiment analysis, emotion detection) and suggest future research directions on model compression, effective knowledge adaptation techniques, neutrality detection and ambivalence handling tasks.

Figurative language (FL) seems ubiquitous in all social media discussion forums and chats, posing extra challenges to sentiment analysis endeavors. Identification of FL schemas in short texts remains largely an unresolved issue in the broader field of natural language processing, mainly due to their contradictory and metaphorical meaning content. The main FL expression forms are sarcasm, irony and metaphor. In the present paper, we employ advanced deep learning methodologies to tackle the problem of identifying the aforementioned FL forms. Significantly extending our previous work (Potamias et al., in: International conference on engineering applications of neural networks, Springer, Berlin, pp 164–175, 2019), we propose a neural network methodology that builds on a recently proposed pre-trained transformer-based network architecture which is further enhanced with the employment and devise of a recurrent convolutional neural network. With this setup, data preprocessing is kept in minimum. The performance of the devised hybrid neural architecture is tested on four benchmark datasets, and contrasted with other relevant state-of-the-art methodologies and systems. Results demonstrate that the proposed methodology achieves state-of-the-art performance under all benchmark datasets, outperforming, even by a large margin, all other methodologies and published studies.