Papers with Complaining
Analyzing the Intensity of Complaints on Social Media (2022.findings-naacl)
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| Challenge: | Prior studies on identifying the existence or the type of complaints focus on building automatic classification models for identifying complaints. |
| Approach: | They propose to measure the intensity of complaints from text using Best-Worst Scaling method to estimate the popularity of posts on social media. |
| Outcome: | The proposed model can estimate the popularity of complaints on social media with best-worst scaling (BWS) method. |
Complaint Identification in Social Media with Transformer Networks (2020.coling-main)
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| Challenge: | Existing work on identifying complaints in social media has focused on feature-based and task-specific neural network models. |
| Approach: | They evaluate a battery of neural models underpinned by transformer networks and combine them with linguistic information to predict complaints. |
| Outcome: | The proposed models outperform state-of-the-art methods on a publicly available dataset achieving a macro F1 up to 87. |
Peeking inside the black box: A Commonsense-aware Generative Framework for Explainable Complaint Detection (2023.acl-long)
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| Challenge: | Complaining is an expression of negative emotions communicated due to a discrepancy between reality and expectations. |
| Approach: | They propose to use an explainable complaint dataset to generate a commonsense-aware generative framework that can predict the complaint cause, severity level, emotion, and polarity of the text. |
| Outcome: | The proposed model can predict the complaint cause, severity level, emotion, and polarity of the text in addition to detecting whether it is a complaint or not. |
Automatically Identifying Complaints in Social Media (P19-1)
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| Challenge: | Complaining is a basic speech act used to express a negative mismatch between reality and expectations in a particular situation. |
| Approach: | They present a systematic analysis of complaints in computational linguistics . they collect annotated data set of written complaints expressed on Twitter . |
| Outcome: | The proposed model achieves predictive performance of up to 79 F1 using distant supervision. |