Sarcasm detection using deep learning and ensemble learning

Goel, Priya; Jain, Rachna; Nayyar, Anand; Singhal, Shruti; Srivastava, Muskan

doi:10.1007/s11042-022-12930-z

Cited by 30 publications

(13 citation statements)

References 47 publications

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“…Ensemble learning and deep learning are two approaches that have dominated the machine learning domain [11]- [13]. Ensemble learning methods train multiple base learners and combine their predictions to obtain improved performance and better generalization ability than the individual base learners [14].…”

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confidence: 99%

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

2022

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Ensemble learning techniques have achieved state-of-the-art performance in diverse machine learning applications by combining the predictions from two or more base models. This paper presents a concise overview of ensemble learning, covering the three main ensemble methods: bagging, boosting, and stacking, their early development to the recent state-of-the-art algorithms. The study focuses on the widely used ensemble algorithms, including random forest, adaptive boosting (AdaBoost), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). An attempt is made to concisely cover their mathematical and algorithmic representations, which is lacking in the existing literature and would be beneficial to machine learning researchers and practitioners.

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confidence: 99%

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

2022

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“…The findings of the research showed that their method performed better in terms of efficiency. Goel et al (2022) recognized and comprehended sarcastic behaviour and patterns. This research intended to bridge the gap between the intelligence of humans and machines.…”

Section: Literature Reviewmentioning

confidence: 99%

Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques

Singh

2024

ADCAIJ

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One of the biggest problems with sentiment analysis systems is sarcasm. The use of implicit, indirect language to express opinions is what gives it its complexity. Sarcasm can be represented in a number of ways, such as in headings, conversations, or book titles. Even for a human, recognizing sarcasm can be difficult because it conveys feelings that are diametrically contrary to the literal meaning expressed in the text. There are several different models for sarcasm detection. To identify humorous news headlines, this article assessed vectorization algorithms and several machine learning models. The recommended hybrid technique using the bag-of-words and TF-IDF feature vectorization models is compared experimentally to other machine learning approaches. In comparison to existing strategies, experiments demonstrate that the proposed hybrid technique with the bag-of-word vectorization model offers greater accuracy and F1-score results.

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“…The number of the collected instances of sarcasm in the considered datasets varied from 1264 to 1055277 [49], [76]. Generally, the higher the number of tweets in the dataset, the higher is the effectiveness of the proposed models.…”

Section: Dataset Collectionmentioning

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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Sarcasm detection using deep learning and ensemble learning

Cited by 30 publications

References 47 publications

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques

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

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