Sarcasm Detection Using Deep Learning With Contextual Features

Razali, Saifullah; Halin, Alfian Abdul; Ye, Lei; Doraisamy, Shyamala; Norowi, Noris Mohd

doi:10.1109/access.2021.3076789

Cited by 39 publications

(20 citation statements)

References 43 publications

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“…Fig. 2 Convolutional Neural Network For text classification, [12] used CNN to generate feature maps, which were then followed by batch normalization layers to improve speed, performance, and stability by re-centering and re-scaling input data. This research also used activation functions and max pooling with a dropout factor of 0.2.Another proposed method was to automatically learn features using a hybrid deep learning model [13].…”

Section: B Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Masked Statement Classification in Conversational Text

Shivanshu Singh, Vidushi Singh, Yogesh

2024

jes

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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

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Section: B Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Masked Statement Classification in Conversational Text

Shivanshu Singh, Vidushi Singh, Yogesh

2024

jes

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“…Another related study [34] used different ML techniques, such as SVM and logistic regression, for classification. The main contribution was combining the features extracted from a Convolutional Neural Network (CNN) architecture with contextual handcrafted features to obtain the most optimal features.…”

Section: Traditional Ml-based Approachesmentioning

confidence: 99%

Text-based Sarcasm Detection on Social Networks: A Systematic Review

Alqahtani¹,

Alhenaki²,

Alsheddi³

2023

IJACSA

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Sarcasm is a sophisticated phenomenon used for conveying a meaning that differs from what is being said, and it is usually used to express displeasure or ridicule others. Sentiment analysis is a process of uncovering the subjective information from a text. Detecting figurative language such as irony or sarcasm, is a focused challenging research field of sentiment analysis. Detecting and understanding the use of sarcasm in social networks could provide businesses and politicians with significant insight, since it reflects people's opinions about certain topics, news, and products. This has especially become relevant recently because sarcastic texts have been trending on social networks and are being posted by millions of active users. As a result of this situation, there is now an increasing amount of research on the detection of sarcasm in social network posts. Many works have been published on sarcasm detection, and they include a wide variety of techniques based on rules, lexicons, traditional machine learning, deep learning, and transformers. However, sarcasm detection is a challenging task due to the ambiguity and non-straightforward nature of sarcastic text. In addition, very few reviews have been conducted on the research in this area. Therefore, this systematic review mainly aims at exploring the newly published sarcasm detection articles on social networks in the years between 2019 and 2022. Several databases were extensively searched, and 30 articles that met the criteria were included. The selected articles were reviewed based on their approaches, datasets, and evaluation metrics. The findings emphasized that deep learning is the most commonly used technique for sarcasm detection in recent literature, and Twitter and F-measure are the most used source and performance metric, respectively. Finally, this article presents a brief discussion regarding the challenges in sarcasm detection and future research directions.

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“…al. identified sarcasm by amalgamating features extracted using deep learning and handcrafted contextual features (19). The handcrafted features are based on contextual description.…”

Section: Learning Based Approachmentioning

confidence: 99%

Sarcasm Detection: A Contemporary Research Affirmation of Recent Literature

Sayeedunnisa¹,

Hegde²,

Khan³

2022

ECS Trans.

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Sarcasm identification is a confined research area in NLP, a specific case of opinion mining where the focal point in the process is identification of sarcasm, instead of sentiment extraction. Sarcasm is a specific type of opinion which is expressed as a negative feeling in form of anger, frustration, or derision veiled by the intense positive words in the text. Detection of sarcasm, which is an elusive problem for machines, has gained wide popularity in the research community in recent years. Accurate identification and analysis of sarcasm improves the performance of sentiment identification models. This manuscript details various sarcasm detection approaches, models and features used, issues, challenges, and further research scope. The various machine learning and deep learning models used to identify sarcasm are detailed in this script.

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Sarcasm Detection Using Deep Learning With Contextual Features

Cited by 39 publications

References 43 publications

Masked Statement Classification in Conversational Text

Masked Statement Classification in Conversational Text

Text-based Sarcasm Detection on Social Networks: A Systematic Review

Sarcasm Detection: A Contemporary Research Affirmation of Recent Literature

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