The speech act of complaining is used by humans to communicate a negative mismatch between reality and expectations as a reaction to an unfavorable situation. Linguistic theory of pragmatics categorizes complaints into various severity levels based on the face-threat that the complainer is willing to undertake. This is particularly useful for understanding the intent of complainers and how humans develop suitable apology strategies. In this paper, we study the severity level of complaints for the first time in computational linguistics. To facilitate this, we enrich a publicly available data set of complaints with four severity categories and train different transformer-based networks combined with linguistic information achieving 55.7 macro F1. We also jointly model binary complaint classification and complaint severity in a multi-task setting achieving new state-of-the-art results on binary complaint detection reaching up to 88.2 macro F1. Finally, we present a qualitative analysis of the behavior of our models in predicting complaint severity levels.
Nanjing University（南京大学）；The Hong Kong Polytechnic University（香港理工大学）
Complaining is a speech act that expresses a negative inconsistency between reality and human expectations. While prior studies mostly focus on identifying the existence or the type of complaints, in this work, we present the first study in computational linguistics of measuring the intensity of complaints from text. Analyzing complaints from such perspective is particularly useful, as complaints of certain degrees may cause severe consequences for companies or organizations. We create the first Chinese dataset containing 3,103 posts about complaints from Weibo, a popular Chinese social media …
University of Sheffield（谢菲尔德大学）；Department of Computer Science, University of Sheffield（谢菲尔德大学计算机科学系）
Complaining is a speech act extensively used by humans to communicate a negative inconsistency between reality and expectations. Previous work on automatically identifying complaints in social media has focused on using feature-based and task-specific neural network models. Adapting state-of-the-art pre-trained neural language models and their combinations with other linguistic information from topics or sentiment for complaint prediction has yet to be explored. In this paper, we evaluate a battery of neural models underpinned by transformer networks which we subsequently combine with linguist…
Automatically Identifying Complaints in Social Media
Bloomberg LP（彭博有限合伙企业）；Politehnica University of Bucharest（布加勒斯特理工大学）；University of Sheffield（谢菲尔德大学）
抱怨是一种基本的言语行为，经常用于人类和计算机中介的沟通，以表达在特定情况下现实和期望之间的负面不匹配。自动识别社交媒体中的投诉对于组织或品牌改善客户体验或开发用于处理和响应投诉的对话系统至关重要。在本文中，我们介绍了第一个系统的分析抱怨在计算语言学。我们收集了一个新的注释数据集，在Twitter上用英语表达的书面投诉。\footnote{Data and code is available here: \url{https://github.com/danielpreotiuc/complaints-social-media}}我们提出了一个广泛的语言分析抱怨作为一种言语行为在社交媒体和训练强大的功能为基础的和神经模型的投诉在九个领域实现预测性能高达79 F1使用远程监督。
MIDAS, IIIT-Delhi（德里印度信息技术学院MIDAS中心）；University of Maryland, College Park（马里兰大学帕克分校）；Bloomberg（彭博公司）；Netaji Subhas Institute of Technology（纳吉吉·萨布哈什理工学院）
Twitter is a social media platform where users express opinions over a variety of issues. Posts offering grievances or complaints can be utilized by private/ public organizations to improve their service and promptly gauge a low-cost assessment. In this paper, we propose an iterative methodology which aims to identify complaint based posts pertaining to the transport domain. We perform comprehensive evaluations along with releasing a novel dataset for the research purposes.
Wright State University（莱特州立大学）；University of South Carolina（南卡罗来纳大学）；Indian Institute of Technology Patna（印度理工学院巴特那分校）
The unprecedented growth of Internet users has resulted in an abundance of unstructured information on social media including health forums, where patients request health-related information or opinions from other users. Previous studies have shown that online peer support has limited effectiveness without expert intervention. Therefore, a system capable of assessing the severity of health state from the patients' social media posts can help health professionals (HP) in prioritizing the user's post. In this study, we inspect the efficacy of different aspects of Natural Language Understanding (…
Semantic Similarity Models for Depression Severity Estimation
Centro de Investigación en Tecnoloxías da Información e as Comunicacións (CITIC)（信息与通信技术研究中心（CITIC））；Universidade da Coruña（拉科鲁尼亚大学）；Hessian Center for AI (hessian.AI)（黑森人工智能中心（hessian.AI））；Technical University of Darmstadt（达姆施塔特工业大学）
Islamic University of Technology（伊斯兰技术大学）；York University（约克大学）；University of Rajshahi（拉杰沙希大学）
Mental health research through data-driven methods has been hindered by a lack of standard typology and scarcity of adequate data. In this study, we leverage the clinical articulation of depression to build a typology for social media texts for detecting the severity of depression. It emulates the standard clinical assessment procedure Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and Patient Health Questionnaire (PHQ-9) to encompass subtle indications of depressive disorders from tweets. Along with the typology, we present a new dataset of 40191 tweets labeled by expert annota…
Birkbeck, University of London（伦敦大学伯贝克学院）；Barnet General Hospital（巴尼特综合医院）
Objective: This study aims to develop an end-to-end natural language processing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts, in order to provide researchers and public health practitioners with additional information on the symptoms, severity and prevalence of the disease rather than to provide an actionable decision at the individual level. Materials and Methods: The text processing pipeline first extracts COVID-19 symptoms and related concepts such as severity, duration, negations, and body parts from patients' posts using conditional random fields.…
SK Innovation（SK创新）；Konkuk University（建国大学）；Korea Institute of Science and Technology（韩国科学技术研究院）
Recent extensively competitive business environment makes companies to keep their eyes on social media, as there is a growing recognition over customer languages (e.g., needs, interests, and complaints) as source of future opportunities. This research avenue analysing social media data has received much attention in academia, but their utilities are limited as most of methods provide retrospective results. Moreover, the increasing number of customer-generated contents and rapidly varying topics have made the necessity of time-aware topic evolution analyses. Recently, several researchers have s…
Indraprastha Institute of Information Technology, Delhi（德里因陀罗普拉斯塔信息技术学院）；National Institute of Technology, Kurukshetra（库鲁克舍特拉国立技术学院）；University of California, Los Angeles（加州大学洛杉矶分校）
The severity of the coronavirus pandemic necessitates the need of effective administrative decisions. Over 4 lakh people in India succumbed to COVID-19, with over 3 crore confirmed cases, and still counting. The threat of a plausible third wave continues to haunt millions. In this ever changing dynamic of the virus, predictive modeling methods can serve as an integral tool. The pandemic has further triggered an unprecedented usage of social media. This paper aims to propose a method for harnessing social media, specifically Twitter, to predict the upcoming scenarios related to COVID-19 cases. …
IndRegBias: A Dataset for Studying Indian Regional Biases in English and Code-Mixed Social Media Comments
Department of Data Science and Engineering, Indian Institute of Science Education and Research（数据科学与工程系，印度科学教育与研究学院）；Oracle Industries AI, Oracle Corporation（Oracle人工智能部，Oracle公司）
Predicting Sentence-Level Factuality of News and Bias of Media Outlets
Institute of Mathematical and Computer Sciences, University of São Paulo（圣保罗大学数学与计算机科学研究所）；Computer Science Department, Federal University of Minas Gerais（米纳斯吉拉斯联邦大学计算机科学系）；Centre for Trusted Internet and Community, National University of Singapore（新加坡国立大学可信互联网与社区中心）
Institute of Mathematical and Computer Sciences, University of São Paulo（圣保罗大学数学与计算机科学学院）；Ribeirão Preto Medical School, University of São Paulo（圣保罗大学里贝朗普雷图医学院）；Federal University of Minas Gerais（米纳斯吉拉斯联邦大学）
Since a lexicon-based approach is more elegant scientifically, explaining the solution components and being easier to generalize to other applications, this paper provides a new approach for offensive language and hate speech detection on social media. Our approach embodies a lexicon of implicit and explicit offensive and swearing expressions annotated with contextual information. Due to the severity of the social media abusive comments in Brazil, and the lack of research in Portuguese, Brazilian Portuguese is the language used to validate the models. Nevertheless, our method may be applied to…
University of New South Wales（新南威尔士大学）；Deakin University（迪肯大学）
The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has lasted for nearly two years and caused unprecedented impacts on people's daily life around the world. Even worse, the emergence of the COVID-19 Delta variant once again puts the world in danger. Fortunately, many countries and companies have started to develop coronavirus vaccines since the beginning of this disaster. Till now, more than 20 vaccines have been approved by the World Health Organization (WHO), bringing light to people besieged by the pandemic. The promotion of COVID-19 vaccination around the world also brings a lot…
DS@GT eRisk 2024: Sentence Transformers for Social Media Risk Assessment
Georgia Institute of Technology（佐治亚理工学院）；Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（RUDN大学））；Joint Institute for Nuclear Research（联合核研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Skövde（舍夫德大学）
