Sarcasm Detection in Hindi-English Code-Mixed Tweets Using Machine Learning Algorithms

Khandagale, Kanhaiyya; Gandhi, Hetal

doi:10.1007/978-981-19-2719-5_20

Cited by 5 publications

(9 citation statements)

References 9 publications

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“…Khandagale et al [35] proposed a system for detecting sarcasm in Hindi-English code-mixed tweets using machine learning and deep learning-based models. The authors compare the performance of various classification models and observe that the Random Forest and Logistic Regression classifiers yielded the highest F-score of 96%.…”

Section: Multilingual Sarcasm Detectionmentioning

confidence: 99%

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: Multilingual Sarcasm Detectionmentioning

confidence: 99%

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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“…This disproportion can degrade the model performance. To reduce the dissymmetry, down sampling was utilized to balance the number of classes [30,31]. To train a deep learning model, a training dataset and a test dataset were randomly separated from the entire dataset in a ratio of 8:2.…”

Section: Data Preprocessingmentioning

confidence: 99%

Robust Sentiment Classification of Metaverse Services Using a Pre-trained Language Model with Soft Voting

2023

KSII TIIS

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Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as "Metaverse" keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.

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“…However, the existing efforts in meme analysis have primarily centered around identifying hateful or offensive memes (Rijhwani et al, 2017;Sharma et al, 2020;Kiela et al, 2020a;Suryawanshi et al, 2020;Hossain et al, 2022;Sharma et al, 2022), or detection of propaganda techniques (Dimitrov et al, 2021) with limited attention given to the identification of persuasive memes. Code-mixing: Furthermore, most of the existing works for memes in the code-mixed settings have been performed on textual data (Kamble and Joshi, 2018;Bali et al, 2014;Mathur et al, 2018;Tang et al, 2020;Bohra et al, 2018). Persuasiveness identification in multimodal, especially in Hinglish scenarios, is primarily unexplored due to inadequate resources and tools.…”

Section: Related Workmentioning

confidence: 99%

“…This research addresses this gap by analyzing the persuasive effectiveness of memes, offering valuable insights for informed and ethical digital discourse. Code-mixing The widespread use of code-mixed memes on social media platforms presents a significant challenge for meme analysis and understanding (Edwards, 1995;Bali et al, 2014;Rijhwani et al, 2017;Kamble and Joshi, 2018;Ghanghor et al, 2021;Hossain et al, 2022). To the best of our knowledge, there is no publicly available dataset for persuasion identification for English-Hindi (Hinglish) code-mixing.…”

Section: Introductionmentioning

confidence: 99%

The Persuasive Memescape: Understanding Effectiveness and Societal Implications of Internet Memes

Kumari,

Shinde,

Ekbal

2023

Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacifi

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Persuasive meme identification is a crucial task in automatically categorizing memes based on their persuasive nature. Memes, being highly influential in online communication, have the ability to shape individuals' attitudes, behaviors, and beliefs, both positively and negatively. They can be utilized to promote positive actions, challenge social norms, and raise awareness, but they can also perpetuate harmful ideologies, spread misinformation, stereotype, and manipulate emotions. In this paper, we are addressing this challenge by empirically investigating three novel tasks, viz. (i) Task 1: Persuasive meme detection, (ii) Task 2: Identification of the effectiveness of persuasive memes, and (iii) Task 3: Identification of persuasion techniques used in persuasive memes. To this end, we make the very first attempt to release a highquality, large-scale dataset, Persuasive_meme 1 , since there is no publicly available such dataset for the Hindi-English code-mixed (Hinglish) domain. 2 We further developed several baseline unimodal and multimodal models for these tasks. Empirical evaluation with respect to both, qualitative and quantitative analysis, on the Persuasive_meme dataset highlight the significance of multimodality in addressing these tasks effectively. Additionally, we discuss the limitations of the current models and emphasize the need for further research to overcome these challenges.

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Sarcasm Detection in Hindi-English Code-Mixed Tweets Using Machine Learning Algorithms

Cited by 5 publications

References 9 publications

A Survey of Sentiment Analysis and Sarcasm Detection

A Survey of Sentiment Analysis and Sarcasm Detection

Robust Sentiment Classification of Metaverse Services Using a Pre-trained Language Model with Soft Voting

The Persuasive Memescape: Understanding Effectiveness and Societal Implications of Internet Memes

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