Sarcasm Detection for Sentiment Analysis: A RNN-Based Approach Using Machine Learning

Rao, Rachana P.; Dayanand, Swathi; Varshitha, K. R.; Kulkarni, Keerti

doi:10.1007/978-981-16-9885-9_4

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Rao et al [21] developed a sarcasm detection model using Twitter data. The dataset used for the experiment is the Twitter headlines dataset.It contains around 30k tweets.…”

Section: Sarcasm Detectionmentioning

confidence: 99%

“…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. The difficulty lies in the nuanced nature of human emotions and expressions conveyed through text, making automatic sarcasm detection a challenging task within natural language processing (NLP) [27,28].…”

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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“…Rao et al [21] developed a sarcasm detection model using Twitter data. The dataset used for the experiment is the Twitter headlines dataset.It contains around 30k tweets.…”

Section: Sarcasm Detectionmentioning

confidence: 99%