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- 01HQNNKVB6T55D856TG9GNHC22 classification P1.
- 01HQNNKVB6T55D856TG9GNHC22 date "2023".
- 01HQNNKVB6T55D856TG9GNHC22 language "eng".
- 01HQNNKVB6T55D856TG9GNHC22 type conference.
- 01HQNNKVB6T55D856TG9GNHC22 hasPart 01HQNNT7NF08B5QSNQK7PDJVCY.pdf.
- 01HQNNKVB6T55D856TG9GNHC22 hasPart urn:uuid:0295ddb8-6c62-46bd-99b3-af9a22ba8191.
- 01HQNNKVB6T55D856TG9GNHC22 subject "Languages and Literatures".
- 01HQNNKVB6T55D856TG9GNHC22 doi "10.18653/v1/2023.findings-emnlp.857".
- 01HQNNKVB6T55D856TG9GNHC22 isbn "9798891760615".
- 01HQNNKVB6T55D856TG9GNHC22 presentedAt urn:uuid:9d1f5e53-4674-4188-acea-f1d54f60f091.
- 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".
- 01HQNNKVB6T55D856TG9GNHC22 author 1BF12826-F0EE-11E1-A9DE-61C894A0A6B4.
- 01HQNNKVB6T55D856TG9GNHC22 author 2B5ADBEA-D0F2-11E9-9DE2-BDAC5607D3EF.
- 01HQNNKVB6T55D856TG9GNHC22 author 7D5875FE-E5BE-11E2-99D2-36B410BDE39D.
- 01HQNNKVB6T55D856TG9GNHC22 author F98A820A-F0ED-11E1-A9DE-61C894A0A6B4.
- 01HQNNKVB6T55D856TG9GNHC22 dateCreated "2024-02-27T16:15:55Z".
- 01HQNNKVB6T55D856TG9GNHC22 dateModified "2025-03-10T08:08:03Z".
- 01HQNNKVB6T55D856TG9GNHC22 editor urn:uuid:0c8e11b5-7c50-4d0f-930f-d2b7ce8f95c4.
- 01HQNNKVB6T55D856TG9GNHC22 editor urn:uuid:446287a3-6d6b-41c4-9d74-b4523f117716.
- 01HQNNKVB6T55D856TG9GNHC22 editor urn:uuid:766b50c5-5cf3-48f4-b426-6e50682e8e52.
- 01HQNNKVB6T55D856TG9GNHC22 name "Misery loves complexity : exploring linguistic complexity in the context of emotion detection".
- 01HQNNKVB6T55D856TG9GNHC22 pagination urn:uuid:d97ce149-7094-4aeb-902b-7e6dfcb05f38.
- 01HQNNKVB6T55D856TG9GNHC22 publisher urn:uuid:d61dc5f5-b9be-4459-a1bc-604f3fa8251e.
- 01HQNNKVB6T55D856TG9GNHC22 sameAs LU-01HQNNKVB6T55D856TG9GNHC22.
- 01HQNNKVB6T55D856TG9GNHC22 sourceOrganization urn:uuid:bf6cb5c5-0de7-47fb-8ea8-a8abfb0f9045.
- 01HQNNKVB6T55D856TG9GNHC22 type P1.