A Review on Sarcasm Detection from Machine-Learning Perspective

The role of predicting sarcasm in the text is known as automatic sarcasm detection. Given the prevalence and challenges of sarcasm in sentiment-bearing text, this is a critical phase in most sentiment analysis tasks. With the increasing popularity and usage of different social media platforms among users around the world, people are using sarcasm more and more in their day-to-day conversations, social media posts and tweets, and it is considered as a way for people to express their sentiment about some certain topics or issues. As a result of the increasing popularity, researchers started to focus their research endeavors on detecting sarcasm from a text in different languages especially the English language. However, the task of sarcasm detection is a challenging task due to the nature of sarcastic texts; which can be relative and significantly differs from one person to another depending on the topic, region, the user's mentality and other factors. In addition to these challenges, sarcasm detection in the Arabic language has its own challenges due to the complexity of the Arabic language, such as being morphologically rich, with many dialects that significantly vary between each other, while also being lowly resourced. In recent years, only few research attempts started tackling the task of sarcasm detection in Arabic, including creating and collecting corpora, organizing workshops and establishing baseline models. This paper intends to create a new humanly annotated Arabic corpus for sarcasm detection collected from tweets, and implementing a new approach for sarcasm detection and quantification in Arabic tweets. The annotation technique followed in this paper is unique in sarcasm detection and the proposed approach tackles the problem as a regression problem instead of classification; i.e., the model attempts to predict the level of sarcasm instead of binary classification.

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.

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

Sarcasm is an expression of emotion, which often expresses criticism and disgust in a positive tone, and we see it everywhere on social networking platforms. Because of this nature, sarcasm can be challenging to process in Natural Language Processing (NLP) Systems. Failing to detect sarcasm correctly can negatively affect the results of natural language processing tasks such as text correct and sentiment analysis. In recent years, there are more and more studies on sarcasm detection, and the research methods are diverse. According to the survey, the methods based on machine learning (ML) and deep learning (DL) account for the majority. Therefore, this paper mainly summarizes the literature on sarcasm detection based on ML and DL methods in recent years. This paper first introduces the application of sarcasm, then expounds the general architecture of sarcasm detection and makes a distinction between machine learning and deep learning. Then the author investigates the relevant literature and compares the results. Finally, the challenges and research directions in this field are summarized. The purpose of this paper is to facilitate the follow-up research of sarcasm detection.

Sentiment analysis (SA) is the procedure of recognizing the emotions related to the data that exist in social networking. The existence of sarcasm in textual data is a major challenge in the efficiency of the SA. Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection, punctuations, and sentiment shift that are vital indicators of sarcasm. With the advent of deep-learning, recent works, leveraging neural networks in learning lexical and contextual features, removing the need for handcrafted feature. In this aspect, this study designs a deep learning with natural language processing enabled SA (DLNLP-SA) technique for sarcasm classification. The proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input data. Besides, the DLNLP-SA technique holds various sub-processes namely preprocessing, feature vector conversion, and classification. Initially, the pre-processing is performed in diverse ways such as single character removal, multi-spaces removal, URL removal, stopword removal, and tokenization. Secondly, the transformation of feature vectors takes place using the N-gram feature vector technique. Finally, mayfly optimization (MFO) with multi-head self-attention based gated recurrent unit (MHSA-GRU) model is employed for the detection and classification of sarcasm. To verify the enhanced outcomes of the DLNLP-SA model, a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.

The article describes distinctive features of a film review as a speech genre which is now one of the most popular genres of the mass media net discourse. The article proves its intertextual character and analyses its lexical and semantic features. Film reviews taken from the following sources: sites of cinema goers: www.imdb.com, www.empireonline.com, www.pluggedin.com ; official sites of film critics (e.g. R. Ebert) https://www.rogerebert.com; official sites of the newspapers: The New York Times https://www.nytimes.com/reviews/movies, The Guardian https://www.theguardian.com/film+tone/reviews and other sites: https://www.imdb.com/search/keyword/?keywords=movie-review, https://www.pluggedin.com/movie-reviews/. The material for analysis comprises about 100 film reviews released in 2020. The reviews are in open access in the Internet. The volume of the analysed material is about 200 pages. The method used to achieve the main objective is interpretation analysis of film reviews. As a result of the theoretical material analysis main directions in the genre research were specified; key distinctive features of a film review were studied. The actuality of the research is determined by the rising interest to net mass media discourse genres. The main objective is to elicit and describe lexical and semantic features of film reviews as a speech genre. The conducted research made it possible to prove the interdiscoursive and poly-discoursive nature of the genre in question and to systematize its lexical and semantic features. The analysis disclosed that intertextuality of a film review is actualized in the interaction of three types of discourse: that of the critic, that of the film and that of other people. Poly-discoursive nature of a film review is expressed through the combination of publicistic, literary and scientific styles features. Another important characteristic is evaluativity represented in emotionally coloured vocabulary. Among other lexical and semantic features are the following: usage of non-specific terms, cliches, rhetorical questions with precedent names, intertextual inserting, various stylistic devices, among which epithets and metaphors are most often used. It’s been observed that a film review is filled with bookish vocabulary as well as stylistically low words and expressions.

Sentiment analysis and sarcasm detection are specialized areas in the field of information retrieval and natural language processing. Sentiment classification is closely correlated with sarcasm detection, where people usually adopt sarcasm to highlight their negative feeling. This paper proposes a novel multi-task deep neural networks for joint sarcasm detection and sentiment analysis (MT_SS). MT_SS train both tasks jointly using bidirectional gated recurrent unit with attention network module to obtain task-specific local feature representation while using convolutional neural networks to obtain global feature representation. The experiments on two datasets show that our proposed model outperforms the state-of-the-art approaches.

Sarcasm detection is a growing field in Natural Language Processing (NLP).Sarcasm is identified using positive or more increased positive words, often with a negative connotation, to insult or mock others.In sentiment analysis, detecting sarcasm in the text has become critical.They reviewed numerous relevant research articles, but due to the telugu language's limited resources, detecting sarcasm in telugu language texts remains challenging.As a result, the sentiment detection model struggles to accurately identify the exact sentiment of a sarcastic statement, necessitating the development of an automated sarcasm detection system.Many researchers have trained and tested various machine learning classification algorithms to identify sarcasm, but these algorithms require a dataset as input, which often contains noise.

Cognitive NLP systems—i.e., NLP systems that make use of behavioral data—augment traditional text-based features with cognitive features extracted from eye-movement patterns, EEG signals, brain imaging, etc. Such extraction of features has been typically manual, as we have seen in the previous chapter. We now contend that manual extraction of features is not good enough to tackle text subtleties that characteristically prevail in complex classification tasks like sentiment analysis and sarcasm detection, and that even the extraction and choice of features should be delegated to the learning system. We introduce a framework to automatically extract cognitive features from the eye-movement data of human readers reading the text and use them as features along with textual features for the tasks of sentiment polarity and sarcasm detection. Our proposed framework is based on Convolutional Neural Network (CNN). The CNN learns features from both gaze and text and uses them to classify the input text. We test our technique on published sentiment and sarcasm labeled datasets, enriched with gaze information, to show that using a combination of automatically learned text and gaze features yields better classification performance over (i) CNN-based systems that rely on text input alone and (ii) existing systems that rely on handcrafted gaze and textual features.

With the ever-growing popularity of microblogging platforms especially Twitter, people are increasingly interested in using these sites to convey their opinions, ideas, and beliefs. People often choose figurative language like irony or sarcasm to share their inherent emotions. Detecting and analyzing such ironic tweets might be beneficial for various natural language processing (NLP) tasks. Since tweet contents are noisy, identifying ironic tweets is a formidable task. In this paper, we present a neural model relying on high-level deep semantic features and hand-crafted features. To extract the higher-level deep features, we apply a combination of multi-kernel convolutional neural network (MKCNN) and long short-term memory (LSTM) network, where MKCNN is placed on top of the LSTM model. Besides, we exploit the lexical, sentimental, and Twitter-specific characteristics to extract a set of hand-crafted features and imputed them to a multilayer perceptron (MLP) module for better tweet representation. Feature representations from the MKCNN-LSTM module and MLP module are then combined and sent to the fully-connected irony detection module. Experiments on the SemEval-2018 irony detection dataset demonstrated the superiority of our presented model over most of the state-of-the-art methods.

Detection of sarcasm is very crucial in today’s world where social media become a major platform of expressing emotions. Sarcastic statements are the statements where the sentiment polarity and contextual meaning are completely contrary. It affects the efficiency and accuracy of present sentiment analysis systems (SAS). Most of the currently available sarcasm detection models such as vector space models, CNN, RNN, etc. consider the raw review text in order to determine the sentiment which ignores the presence of negation, lexical ambiguity, and irony created by general facts. Due to this, detecting sarcasm may not be very accurate. To improve the accuracy of sarcasm detection and to better understand the context, the model proposed integrates the ratings, reviews, and emojis. The performance of the proposed system is evaluated using annotator-agreement methods with the metrics such as F1 score, Precision, Recall, and Accuracy. The performance shows that integrating more features enhances the accuracy by a considerable margin as compared to previously defined methodologies.

Although common sense and connotative knowledge come naturally to most people, computers still struggle to perform well on tasks for which such extratextual information is required. Automatic approaches to sentiment analysis and irony detection have revealed that the lack of such world knowledge undermines classification performance. In this article, we therefore address the challenge of modeling implicit or prototypical sentiment in the framework of automatic irony detection. Starting from manually annotated connoted situation phrases (e.g., “flight delays,” “sitting the whole day at the doctor’s office”), we defined the implicit sentiment held towards such situations automatically by using both a lexico-semantic knowledge base and a data-driven method. We further investigate how such implicit sentiment information affects irony detection by assessing a state-of-the-art irony classifier before and after it is informed with implicit sentiment information.

Sentiment analysis plays a pivotal role in understanding the sentiments and opinions expressed in textual data, offering valuable insights into various domains, including tourism. In this study, we present a comprehensive review of sentiment analysis techniques applied to tourism reviews using machine learning algorithms. The abundance of user-generated content on tourism platforms has made sentiment analysis an indispensable tool for businesses and researchers alike. By leveraging machine learning algorithms, researchers can extract sentiments from vast amounts of textual data efficiently and accurately. This review outlines the key methodologies and approaches utilized in sentiment analysis of tourism reviews. It discusses preprocessing techniques such as text tokenization, stop-word removal, and stemming, which are crucial for preparing textual data for analysis. Furthermore, it examines various machine learning algorithms employed for sentiment classification, including Naive Bayes, Support Vector Machines, and Recurrent Neural Networks. Additionally, the review delves into feature extraction methods such as bag-of-words, TF-IDF, and word embeddings, highlighting their impact on sentiment analysis accuracy. Moreover, it explores the challenges and limitations associated with sentiment analysis in the tourism domain, such as sarcasm detection and language nuances.

Sarcasm is a nuanced form of speech extensively employed in various online platforms such as social networks, micro-blogs etc. and sarcasm detection refers to predicting whether the text is sarcastic or not. Detecting sarcasm in text is among the major issues facing sentiment analysis. In the last decade, researchers have been working rigorously on sarcasm detection so as to amend the performance of automatic sentiment analysis of data. In this paper, a supervised machine learning (ML) approach, which learns from different categories of features and their combinations, is presented. These feature sets are employed to classify instances as sarcastic and not-sarcastic using different classifiers. In particular, the impact of sarcastic patterns based on POS tags has been investigated and the results show that they are not useful as a feature set for detecting sarcasm as compared to content words and function words. Also, the Naive Bayes classifier outperforms all other classifiers used.

Portuguese is a low-resource language, where a few works developed corpora for specific Natural Language Processing tasks, such as sarcasm and irony detection, sentiment analysis and others. In this work, we developed a corpus in the Portuguese language to sarcasm and irony detection task. In the future, we intend to develop a tool to recognize sarcasm and irony and we intend to use the corpus presented in this article.