Sarcasm In Text Research Articles

Decoding sarcasm: unveiling nuances in newspaper headlines

This study navigates the intricate landscape of sarcasm detection within the condensed confines of newspaper titles, addressing the nuanced challenge of decoding layered meanings. Leveraging natural language processing (NLP) techniques, we explore the efficacy of various machine learning models—linear regression, support vector machines (SVM), random forest, na¨ıve Bayes multinomial, and gaussian na¨ıve Bayes—tailored for sarcasm detection. Our investigation aims to provide insights into sarcasm within the succinct framework of newspaper titles, offering a comparative analysis of the selected models. We highlight the varied strengths and weaknesses of these models. Random forest exhibits superior performance, achieving a remarkable 94% accuracy in accurately identifying sarcasm in text. It is closely trailed by SVM with 90% accuracy and logistic regression with 83% accuracy.

Masked Statement Classification in Conversational Text

Sarcasm can be really tricky for computer programs that analyze text to understand people’s feelings and opinions. This is because sarcasm involves saying something with the opposite meaning of what you actually feel, often to make fun of a situation. When computers analyze text, they focus on the words used rather than the intended meaning, which can lead to mistakes in how the text is categorized. Sarcasm also creates problems when trying to figure out the author’s true emotions in tasks like sentiment analysis. So, there is a need of a way to identify sarcasm in text and correct the sentiment it conveys. Recent studies have employed deep learning and machine learning methods to identify sarcasm. Deep learning is quite powerful, but traditional machine learning methods are still widely used and can work as well if trained properly. This study aims to compare different algorithms like Long Short Term Memory (LSTM), K-Nearest Neighbors (KNN), Computational Neural Network(CNN), and Artificial Neural Network(ANN) etc to see how well they work in sarcasm detection. The survey examined several systems and algorithms for detecting sarcasm, as well as any faults or drawbacks associated with these approaches. It also discusses various important challenges and future directions for sarcasm detection research in order to provide useful insights in this field

A Survey of Sentiment Analysis and Sarcasm Detection

In recent years, more people have been using the internet and social media to express their opinions on various subjects, such as institutions, services, or specific ideas. This increase highlights the importance of developing automated tools for accurate sentiment analysis. Moreover, addressing sarcasm in text is crucial, as it can significantly impact the efficacy of sentiment analysis models. This paper aims to provide a comprehensive overview of the conducted research on sentiment analysis and sarcasm detection, focusing on the time from 2018 to 2023. It explores the challenges faced and the methods used to address them. It conducts a comparison of these methods. It also aims to identify emerging trends that will likely influence the future of sentiment analysis and sarcasm detection, ensuring their continued effectiveness. This paper enhances the existing knowledge by offering a comprehensive analysis of 40 research works, evaluating performance, addressing multilingual challenges, and highlighting future trends in sarcasm detection and sentiment analysis. It is a valuable resource for researchers and experts interested in the field, facilitating further advancements in sentiment analysis techniques and applications. It categorizes sentiment analysis methods into ML, lexical, and hybrid approaches, highlighting deep learning, especially Recurrent Neural Networks (RNNs), for effective textual classification with labeled or unlabeled data.

An Efficient Sarcasm Detection using Linguistic Features and Ensemble Machine Learning

Sarcasm detection in written text has emerged as a significant research area within natural language processing (NLP). Sarcasm, characterized by conveying the opposite of the intended meaning often for humor, irony, or ridicule, poses a challenge due to its contextual and tonal nuances. This study investigates the application of machine learning methods to detect sarcasm in text due to its potential to reverse the overall sentiment expressed in a sentence. A total of 13 linguistic manually crafted features related to text meaning, word usage, lexical diversity, and readability are extracted. These features are then employed to train a variety of machine learning models including Gradient Boosting, Decision Tree, Random Forest, Support Vector Machine, Gaussian Nave Bayes, K-Nearest Neighbor, and Logistic Regression classifiers. Additionally, an Ensemble Model and a Dense Neural Network is developed, both trained on the extracted handcrafted features to showcase performance. The results reveal that the suggested ensemble model achieves a peak accuracy of 93% in sarcasm detection. The amalgamation of these 13 linguistic features enhances model performance when compared to other contemporary models, exhibiting an improvement of up to 5% in terms of F1-score using the publicly available gold standard News Headline Dataset.

Detection of Sarcasm Using Bi-Directional RNN Based Deep Learning Model in Sentiment Analysis

Detecting sarcasm in text can be challenging, but it's an opportunity to exercise the ability to consider various perspectives and aspects. Considering multiple viewpoints while analysing sentences can lead to comprehensive results and constructive discussions. Sentiment analysis algorithms are constantly improving and becoming more accurate in identifying sarcasm, even in implicit expressions where the sarcasm is not explicitly stated. It's exciting to have the opportunity to detect sarcasm conveyed through subtle cues, as it adds another layer of complexity to the task. The field of sarcasm detection has made great strides with the use of supervised classification models that can accurately distinguish between sarcastic and non-sarcastic sentences through labelled data. This approach shows promise for identifying explicitly incongruous statements, and there is an increasing need for techniques that can successfully detect sarcasm in sentences with implicit sentiment incongruity. This study employs a deep learning model to tackle the challenge of distinguishing implicit sarcasm. The model being used is a recurrent neural network (RNN), which has the ability to retain numerical representations of previously processed information. The proposed model is designed to effectively identify sarcasm by capturing context-based incongruity within the presented text, which is a crucial factor. The proposed model, Proposed LSTM (P-LSTM), effectively captures the dependencies among words in both suffixes and prefixes by scanning the given sentences bi-directionally. The classification is organised into two levels: emotional and semantic. The suggested approach offers a way to accurately evaluate and categories tweets based on their sarcastic state. Excitingly, the proposed method is being evaluated through experiments on two automatically annotated datasets and two manually annotated datasets to determine its effectiveness. The suggested model is compared to numerous state-of-the-art methods, highlighting its efficacy. The findings demonstrate that the suggested model is highly effective for sarcasm detection compared to other methods currently in use. The suggested model has great potential in recognising implicit sarcasm by utilising deep learning and context-based incongruity analysis, which could lead to significant improvements over existing methods.

Sarcasm Detection of Sentiments in Telugu Language

Sarcasm is usually used by people to either tease/irritate others or simply for comic purposes. The presence of sarcasm becomes certain as it is difficult to be identified by basic sentiment analysis method. Sarcasm detection is addressed with various rule-based methods, statistical approaches, and classifiers in machine learning , most of these are introduced to identify sarcasm in text written in English as it is a popular language on the internet. Although the groundwork done on sarcasm detection on various Indian languages like Telugu is limited. Hence, this paper presents a Deep learning model based on neural networks to detect sarcasm in Telugu news headlines taken from various websites . The proposed model comprises of Convolutional Neural Networks(CNN) and next a Long short-term memory(LSTM) Network which is a modified version of Recurrent neural networks (RNN) and lastly a fully connected dense layer is added to classify the sentiments into sarcastic and non-sarcastic. A pre-trained word embeddings GloVe are used in the model

Automatic Sarcasm Detection

Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing text. Beginning with an approach that used speech-based features, automatic sarcasm detection has witnessed great interest from the sentiment analysis community. This article is a compilation of past work in automatic sarcasm detection. We observe three milestones in the research so far: semi-supervised pattern extraction to identify implicit sentiment, use of hashtag-based supervision, and incorporation of context beyond target text. In this article, we describe datasets, approaches, trends, and issues in sarcasm detection. We also discuss representative performance values, describe shared tasks, and provide pointers to future work, as given in prior works. In terms of resources to understand the state-of-the-art, the survey presents several useful illustrations—most prominently, a table that summarizes past papers along different dimensions such as the types of features, annotation techniques, and datasets used.

Hybrid human–machine information systems: Challenges and opportunities

Micro-task Crowdsourcing has been used for different purposes: creating training data for machine learning algorithms, relevance judgments for evaluation of information systems, sentiment analysis, language translation, etc. In this paper we focus on the use of crowdsourcing as core component of data-driven systems. The creation of hybrid human–machine systems is a highly promising direction as it allows leveraging both the scalability of machines over large amounts of data as well as keeping the quality of human intelligence in the loop to finally obtain both efficiency and effectiveness in data processing applications.Such a hybrid approach is a great opportunity to develop systems that are more powerful than purely machine-based ones. For example, it is possible to build systems that can understand sarcasm in text at scale. However, when designing such systems it is critical to take into account a number of dimensions related to human behavior as humans become a component of the overall process.In this paper, we overview existing hybrid human–machine systems presenting commonalities in the approaches taken by different research communities. We summarize the key challenges that one has to face in developing such systems as well the opportunities and the open research directions to make such approaches the best way to process data in the future.

Distinguishing Sarcasm From Literal Language: Evidence From Books and Blogging

Sarcasm production and comprehension have been traditionally described in terms of pragmatic factors. Lexical cues have received less attention, but they may be important potential indicators. A major obstacle to examining such features is determining sarcastic intent. One solution is to analyze statements explicitly marked as being sarcastic. This study examined Twitter postings marked with #sarcasm as well as dialog from Google Books containing the phrase “said sarcastically.” We used word counting and part-of-speech tagging to compare specific lexical features of the explicitly-marked sarcastic statements to statements by the same author not marked as sarcastic. Our results broadly support the Lexical Cues Hypothesis—certain word-level cues, such as interjections and positive affect terms, are stereotypic of sarcasm. A model incorporating these features performed comparably to human raters in making sarcastic versus nonsarcastic judgments. This finding shows promise for future work toward automatically identifying sarcasm in text.