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Class-biased sarcasm detection using BiLSTM variational autoencoder-based synthetic oversampling

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A Correction to this article was published on 27 March 2023

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Abstract

Recent research works have established the importance of sarcasm detection in the domain of sentiment analysis. Automatic sarcasm detection using social media data is a challenging task in the presence of imbalanced classes. Real-life social media data often suffer from this problem of class imbalance resulting in dramatical degradation of the performance of classification models attempting to detect sarcasm. Motivated by this, in the current article, a Bi-LSTM variational autoencoder model has been proposed to alleviate the problem of imbalanced classes in social media datasets targeted to train sarcasm detection models. The proposed BVA model is trained with a large corpus of sarcastic and non-sarcastic tweets to obtain the most suitable latent space representation of the same. These inherently class-biased latent vectors are then oversampled using synthetic minority oversampling techniques. The quality of the proposed method is established by training and testing a set of well-known classifiers in terms of precision, recall, F1-score, AUC, and G-mean. Extensive experiments reveal that the proposed BVA model combined with oversampling techniques can improve classifier performance for sarcasm detection to a greater extent.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India

    Sankhadeep Chatterjee & Asit Kumar Das

  2. Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India

    Saranya Bhattacharjee

  3. Department of Computing Science, University of Alberta, Edmonton, Canada

    Kushankur Ghosh

  4. Department of Electronics and Communication Engineering, Budge Budge Institute of Technology, Budge Budge, Kolkata, 700137, West Bengal, India

    Soumen Banerjee

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  1. Sankhadeep Chatterjee
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  2. Saranya Bhattacharjee
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  3. Kushankur Ghosh
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  4. Asit Kumar Das
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  5. Soumen Banerjee
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Contributions

Sankhadeep Chatterjee was involved in the conceptualization; Sankhadeep Chatterjee contributed to the methodology; Sankhadeep Chatterjee and Kushankur Ghosh assisted in the formal analysis and investigation; Saranya Bhattacharjee and Sankhadeep Chatterjee contributed to the simulation; Sankhadeep Chatterjee, Saranya Bhattacharjee, and Kushankur Ghosh assisted in writing—original draft preparation; Soumen Banerjee and Sankhadeep Chatterjee were involved in writing—review and editing; Asit Kumar Das contributed to the resources; Asit Kumar Das and Soumen Banerjee were involved in the supervision.

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Correspondence to Sankhadeep Chatterjee.

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Chatterjee, S., Bhattacharjee, S., Ghosh, K. et al. Class-biased sarcasm detection using BiLSTM variational autoencoder-based synthetic oversampling. Soft Comput 27, 5603–5620 (2023). https://doi.org/10.1007/s00500-023-07956-w

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