Sarcasm Detection in Social Media Using Hybrid Deep Learning and Machine Learning Approaches

Sharma, Tanya; Rani, Neeraj; Mittal, Aakriti; Rathee, Nisha

doi:10.1007/978-3-030-95711-7_38

Cited by 1 publication

(2 citation statements)

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“…Tanya et al [17] proposed a hybrid approach combining deep learning and traditional machine learning methods for detecting sarcasm in social media, using the News Headlines Dataset. The authors pre-process the dataset by removing usernames, stop words, and non-alphanumeric characters.…”

Section: Sarcasm Detectionmentioning

confidence: 99%

“…It explores research areas such as data mining and machine learning within the context of NLP [12,13]. Sarcasm [14][15][16][17][18], a statement conveying the as it can invert the true sentiment of a statement. Despite being a popular research topic, sarcasm detection [19][20][21][22][23][24][25][26] is crucial as sentiment analysis can misinterpret sarcastic sentences, leading to inaccurate sentiment classifications.…”

Section: Introductionmentioning

confidence: 99%

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A Survey of Sentiment Analysis and Sarcasm Detection

Derbala Yacoub,

O. Slim,

Elsayed Aboutabl

2024

Int. j. electr. comput. eng. syst. (Online)

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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.

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Section: Sarcasm Detectionmentioning

confidence: 99%