| 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. |
Similar Papers
Complaint Identification in Social Media with Transformer Networks (2020.coling-main)
Copied to clipboard
| 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. |
Modeling the Severity of Complaints in Social Media (2021.naacl-main)
Copied to clipboard
| Challenge: | Complaining is a speech act used by humans to communicate a negative mismatch between reality and expectations . recent work on modeling complaints in natural language processing (NLP) has focused on distinguishing complaints from non-complaints in social media. |
| Approach: | They propose to classify complaints into various severity levels based on the face-threat that the complainer is willing to undertake and their purpose. |
| Outcome: | The proposed model achieves 55.7 macro F1 on binary complaint classification and 88.2 macro F1. |
Analyzing the Intensity of Complaints on Social Media (2022.findings-naacl)
Copied to clipboard
| 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 Analysis and Classification for Economic and Food Safety (D19-51)
Copied to clipboard
João Filgueiras, Luís Barbosa, Gil Rocha, Henrique Lopes Cardoso, Luís Paulo Reis, João Pedro Machado, Ana Maria Oliveira
| Challenge: | Governmental institutions are using artificial intelligence to deal with specific problems and exploit their huge amounts of structured and unstructured information. |
| Approach: | They propose to use natural language processing and machine learning to classify complaints . they use feature-based approaches and traditional classifiers to analyze complaints based on citizen feedback . |
| Outcome: | The proposed methods have accuracy scores above 70% and can be used to improve public services. |
Peeking inside the black box: A Commonsense-aware Generative Framework for Explainable Complaint Detection (2023.acl-long)
Copied to clipboard
| 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. |
Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment (N19-3)
Copied to clipboard
| Challenge: | #MeToo movement provides platform to narrate personal experiences of sexual harassment. |
| Approach: | They propose a three-part ULMFiT architecture to tackle text subtleties in a classification task . they propose to annotate a manually annotated real-world dataset to test their approach . |
| Outcome: | The proposed model outperforms existing models that rely on handcrafted stylistic features and is more accurate than generic models. |
Representing Social Media Users for Sarcasm Detection (D18-1)
Copied to clipboard
| Challenge: | Existing annotated corpus of Reddit comments is limited by available annotation methods. |
| Approach: | They propose a Bayesian approach that directly represents authors’ propensities to be sarcastic and a dense embedding approach that can learn interactions between the author and the text. |
| Outcome: | The proposed approach performs better in homogeneous contexts, whereas the dense embeddings prove valuable in more diverse contexts. |
A Computational Exploration of Pejorative Language in Social Media (2021.findings-emnlp)
Copied to clipboard
| Challenge: | In this paper, we examine the problem of pejorative language, an under-explored topic in computational linguistics. |
| Approach: | They propose to automatically disambiguate pejorative usage in social media . they leverage online dictionaries to build a multilingual lexicon of pejorativ terms . |
| Outcome: | The proposed model can automatically disambiguate pejorative usage in social media posts . the proposed model is based on dictionaries and tweets . |
Federated Meta-Learning for Emotion and Sentiment Aware Multi-modal Complaint Identification (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on complaint identification are limited to text. |
| Approach: | They propose a meta-learning-based multi-modal multi-task framework for identifying complaints using emotion recognition and sentiment analysis as auxiliary tasks. |
| Outcome: | The proposed framework outperforms baselines and state-of-the-art approaches in centralized and federated meta-learning settings. |
Automatic Identification and Classification of Bragging in Social Media (2022.acl-long)
Copied to clipboard
| Challenge: | Bragging is a speech act employed to build a favorable self-image through positive statements about oneself. |
| Approach: | They propose to use tweets annotated for bragging to build a model that can predict bragging with macro F1 up to 72.42 and 35.95 for binary and multi-class bragging classification tasks respectively. |
| Outcome: | The proposed models predict bragging with macro F1 up to 72.42 and 35.95 in binary and multi-class classification tasks respectively. |