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Misery loves complexity : exploring linguistic complexity in the context of emotion detection

Pranaydeep Singh (UGent) , Luna De Bruyne (UGent) , Orphée De Clercq (UGent) and Els Lefever (UGent)
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Abstract
Given the omnipresence of social media in our society, thoughts and opinions are being shared online in an unprecedented manner. This means that both positive and negative emotions can be equally and freely expressed. However, the negativity bias posits that human beings are inherently drawn to and more moved by negativity and, as a consequence, negative emotions get more traffic. Correspondingly, when writing about emotions this negativity bias could lead to expressions of negative emotions that are linguistically more complex. In this paper, we attempt to use readability and linguistic complexity metrics to better understand the manifestation of emotions on social media platforms like Reddit based on the widely-used GoEmotions dataset. We demonstrate that according to most metrics, negative emotions indeed tend to generate more complex text than positive emotions. In addition, we examine whether a higher complexity hampers the automatic identification of emotions. To answer this question, we fine-tuned three state-of-the-art transformers (BERT, RoBERTa, and SpanBERT) on the same emotion detection dataset. We demonstrate that these models often fail to predict emotions for the more complex texts. More advanced LLMs like RoBERTa and SpanBERT also fail to improve by significant margins on complex samples. This calls for a more nuanced interpretation of the emotion detection performance of transformer models. We make the automatically annotated data available for further research at: https://huggingface.co/datasets/pranaydeeps/CAMEO

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MLA
Singh, Pranaydeep, et al. “Misery Loves Complexity : Exploring Linguistic Complexity in the Context of Emotion Detection.” Findings of the Association for Computational Linguistics : EMNLP 2023, edited by Houda Bouamor et al., Association for Computational Linguistics, 2023, pp. 12871–80, doi:10.18653/v1/2023.findings-emnlp.857.
APA
Singh, P., De Bruyne, L., De Clercq, O., & Lefever, E. (2023). Misery loves complexity : exploring linguistic complexity in the context of emotion detection. In H. Bouamor, J. Pino, & K. Bali (Eds.), Findings of the Association for Computational Linguistics : EMNLP 2023 (pp. 12871–12880). https://doi.org/10.18653/v1/2023.findings-emnlp.857
Chicago author-date
Singh, Pranaydeep, Luna De Bruyne, Orphée De Clercq, and Els Lefever. 2023. “Misery Loves Complexity : Exploring Linguistic Complexity in the Context of Emotion Detection.” In Findings of the Association for Computational Linguistics : EMNLP 2023, edited by Houda Bouamor, Juan Pino, and Kalika Bali, 12871–80. Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.857.
Chicago author-date (all authors)
Singh, Pranaydeep, Luna De Bruyne, Orphée De Clercq, and Els Lefever. 2023. “Misery Loves Complexity : Exploring Linguistic Complexity in the Context of Emotion Detection.” In Findings of the Association for Computational Linguistics : EMNLP 2023, ed by. Houda Bouamor, Juan Pino, and Kalika Bali, 12871–12880. Association for Computational Linguistics. doi:10.18653/v1/2023.findings-emnlp.857.
Vancouver
1.
Singh P, De Bruyne L, De Clercq O, Lefever E. Misery loves complexity : exploring linguistic complexity in the context of emotion detection. In: Bouamor H, Pino J, Bali K, editors. Findings of the Association for Computational Linguistics : EMNLP 2023. Association for Computational Linguistics; 2023. p. 12871–80.
IEEE
[1]
P. Singh, L. De Bruyne, O. De Clercq, and E. Lefever, “Misery loves complexity : exploring linguistic complexity in the context of emotion detection,” in Findings of the Association for Computational Linguistics : EMNLP 2023, Singapore, 2023, pp. 12871–12880.
@inproceedings{01HQNNKVB6T55D856TG9GNHC22,
  abstract     = {{Given the omnipresence of social media in our society, thoughts and opinions are being shared online in an unprecedented manner. This means that both positive and negative emotions can be equally and freely expressed. However, the negativity bias posits that human beings are inherently drawn to and more moved by negativity and, as a consequence, negative emotions get more traffic. Correspondingly, when writing about emotions this negativity bias could lead to expressions of negative emotions that are linguistically more complex. In this paper, we attempt to use readability and linguistic complexity metrics to better understand the manifestation of emotions on social media platforms like Reddit based on the widely-used GoEmotions dataset. We demonstrate that according to most metrics, negative emotions indeed tend to generate more complex text than positive emotions. In addition, we examine whether a higher complexity hampers the automatic identification of emotions. To answer this question, we fine-tuned three state-of-the-art transformers (BERT, RoBERTa, and SpanBERT) on the same emotion detection dataset. We demonstrate that these models often fail to predict emotions for the more complex texts. More advanced LLMs like RoBERTa and SpanBERT also fail to improve by significant margins on complex samples. This calls for a more nuanced interpretation of the emotion detection performance of transformer models. We make the automatically annotated data available for further research at: https://huggingface.co/datasets/pranaydeeps/CAMEO}},
  author       = {{Singh, Pranaydeep and De Bruyne, Luna and De Clercq, Orphée and Lefever, Els}},
  booktitle    = {{Findings of the Association for Computational Linguistics : EMNLP 2023}},
  editor       = {{Bouamor, Houda and Pino, Juan and Bali, Kalika}},
  isbn         = {{9798891760615}},
  language     = {{eng}},
  location     = {{Singapore}},
  pages        = {{12871--12880}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{Misery loves complexity : exploring linguistic complexity in the context of emotion detection}},
  url          = {{http://doi.org/10.18653/v1/2023.findings-emnlp.857}},
  year         = {{2023}},
}

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