A Novel Sentiment Analysis Engine for Preliminary Depression Status Estimation on Social Media
University of Cambridge（剑桥大学）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）；Indraprastha Institute of Information Technology, Delhi（德里英迪拉普拉斯塔信息技术学院）
Text sentiment analysis for preliminary depression status estimation of users on social media is a widely exercised and feasible method, However, the immense variety of users accessing the social media websites and their ample mix of vocabularies makes it difficult for commonly applied deep learning-based classifiers to perform. To add to the situation, the lack of adaptability of traditional supervised machine learning could hurt at many levels. We propose a cloud-based smartphone application, with a deep learning-based backend to primarily perform depression detection on Twitter social media. The backend model consists of a RoBERTa based siamese sentence classifier that compares a given tweet (Query) with a labeled set of tweets with known sentiment ( Standard Corpus ). The standard corpus is varied over time with expert opinion so as to improve the model's reliability. A psychologist ( with the patient's permission ) could leverage the application to assess the patient's depression status prior to counseling, which provides better insight into the mental health status of a patient. In addition, to the same, the psychologist could be referred to cases of similar characteristics, which could in turn help in more effective treatment. We evaluate our backend model after fine-tuning it on a publicly available dataset. The find tuned model is made to predict depression on a large set of tweet samples with random noise factors. The model achieved pinnacle results, with a testing accuracy of 87.23% and an AUC of 0.8621.
University of Ottawa（渥太华大学）；Dalhousie University（达尔豪斯大学）
Reddit.com is a popular social media platform among young people. Reddit users share their stories to seek support from other users, especially during the Covid-19 pandemic. Messages posted on Reddit and their content have provided researchers with opportunity to analyze public concerns. In this study, we analyzed sentiments of COVID-related messages posted on r/Depression. Our study poses the following questions: a) What are the common topics that the Reddit users discuss? b) Can we use these topics to classify sentiments of the posts? c) What matters concern people more during the pandemic? …
Multimodal sentiment analysis and depression estimation are two important research topics that aim to predict human mental states using multimodal data. Previous research has focused on developing effective fusion strategies for exchanging and integrating mind-related information from different modalities. Some MLP-based techniques have recently achieved considerable success in a variety of computer vision tasks. Inspired by this, we explore multimodal approaches with a feature-mixing perspective in this study. To this end, we introduce CubeMLP, a multimodal feature processing framework based …
Kozminski University（科兹明斯基大学）；University of Warsaw（华沙大学）
Sentiment and lexical analyses are widely used to detect depression or anxiety disorders. It has been documented that there are significant differences in the language used by a person with emotional disorders in comparison to a healthy individual. Still, the effectiveness of these lexical approaches could be improved further because the current analysis focuses on what the social media entries are about, and not how they are written. In this study, we focus on aspects in which these short texts are similar to each other, and how they were created. We present an innovative approach to the depr…
How could a product or service is reasonably evaluated by anyone in the shortest time? A million dollar question but it is having a simple answer: Sentiment analysis. Sentiment analysis is consumers review on products and services which helps both the producers and consumers (stakeholders) to take effective and efficient decision within a shortest period of time. Producers can have better knowledge of their products and services through the sentiment analysis (ex. positive and negative comments or consumers likes and dislikes) which will help them to know their products status (ex. product lim…
Depression detection from Social Media Bangla Text Using Recurrent Neural Networks
University of Maryland Baltimore County（马里兰大学巴尔的摩分校）；Bangladesh University of Engineering & Technology（孟加拉国工程技术大学）；University of Dhaka（达卡大学）；Jahangirnagar University（贾汉吉尔纳加尔大学）；Jagannath University（贾格纳特大学）
Social media is increasingly used by humans to express their feelings and opinions in the form of short text messages. Detecting sentiments in the text has a wide range of applications including identifying anxiety or depression of individuals and measuring well-being or mood of a community. Sentiments can be expressed in many ways that can be seen such as facial expression and gestures, speech and by written text. Sentiment Analysis in text documents is essentially a content-based classification problem involving concepts from the domains of Natural Language Processing as well as Machine Lear…
Central University of Kashmir（克什米尔中央大学）；IIT Patna（印度理工学院巴特那分校）；University of Kashmir（克什米尔大学）
Textual emotional intelligence is playing a ubiquitously important role in leveraging human emotions on social media platforms. Social media platforms are privileged with emotional content and are leveraged for various purposes like opinion mining, emotion mining, and sentiment analysis. This data analysis is also levered for the prevention of online bullying, suicide prevention, and depression detection among social media users. In this article, we have designed an automatic depression detection of online social media users by analyzing their social media behavior. The designed depression det…
University of Exeter（埃克塞特大学）；Max Planck Institute for Human Development（马克斯·普朗克人类发展研究所）；University of Warwick（华威大学）
Most current affect scales and sentiment analysis on written text focus on quantifying valence (sentiment) -- the most primary dimension of emotion. However, emotions are broader and more complex than valence. Distinguishing negative emotions of similar valence could be important in contexts such as mental health. This project proposes a semi-supervised machine learning model (DASentimental) to extract depression, anxiety and stress from written text. First, we trained the model to spot how sequences of recalled emotion words by $N=200$ individuals correlated with their responses to the Depres…
Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh（匹兹堡大学）
During the perinatal period, psychosocial health risks, including depression and intimate partner violence, are associated with serious adverse health outcomes for parents and children. To appropriately intervene, healthcare professionals must first identify those at risk, yet stigma often prevents people from directly disclosing the information needed to prompt an assessment. We examine indirect methods of eliciting and analyzing information that could indicate psychosocial risks. Short diary entries by peripartum women exhibit thematic patterns, extracted by topic modeling, and emotional per…
Sentiment Analysis is an important algorithm in Natural Language Processing which is used to detect sentiment within some text. In our project, we had chosen to work on analyzing reviews of various drugs which have been reviewed in form of texts and have also been given a rating on a scale from 1-10. We had obtained this data set from the UCI machine learning repository which had 2 data sets: train and test (split as 75-25\%). We had split the number rating for the drug into three classes in general: positive (7-10), negative (1-4) or neutral(4-7). There are multiple reviews for the drugs that…
This project addresses the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users - out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflecti…
The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has caused unprecedented impacts to people's daily life around the world. Various measures and policies such as lockdown and social-distancing are implemented by governments to combat the disease during the pandemic period. These measures and policies as well as virus itself may cause different mental health issues to people such as depression, anxiety, sadness, etc. In this paper, we exploit the massive text data posted by Twitter users to analyse the sentiment dynamics of people living in the state of New South Wales (NSW) in Aust…
University of New South Wales（新南威尔士大学）；Indian Institute of Technology Guwahati（印度理工学院古瓦哈提分校）
Social scientists and psychologists take interest in understanding how people express emotions and sentiments when dealing with catastrophic events such as natural disasters, political unrest, and terrorism. The COVID-19 pandemic is a catastrophic event that has raised a number of psychological issues such as depression given abrupt social changes and lack of employment. Advancements of deep learning-based language models have been promising for sentiment analysis with data from social networks such as Twitter. Given the situation with COVID-19 pandemic, different countries had different peaks…
Birmingham City University（伯明翰城市大学）；University of Bristol（布里斯托大学）
In 2016, United Kingdom (UK) citizens voted to leave the European Union (EU), which was officially implemented in 2020. During this period, UK residents experienced a great deal of uncertainty around the UK's continued relationship with the EU. Many people have used social media platforms to express their emotions about this critical event. Sentiment analysis has been recently considered as an important tool for detecting mental well-being in Twitter contents. However, detecting the psychological distress status in political-related tweets is a challenging task due to the lack of explicit sent…
University of Rochester（罗切斯特大学）；Microsoft Research AI（微软研究院AI）
Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis. We first learn bi-sense emoji embeddings under pos…
Detect Depression from Social Networks with Sentiment Knowledge Sharing
University of Science and Technology of China（中国科学技术大学）；Anhui Medical University（安徽医科大学）；the Second Affiliated Hospital of Anhui Medical University（安徽医科大学第二附属医院）；Anhui University of Technology（安徽工业大学）
University of Liberal Arts Bangladesh (ULAB)（孟加拉国自由艺术大学）
Sentiment Analysis (SA) is an action research area in the digital age. With rapid and constant growth of online social media sites and services, and the increasing amount of textual data such as - statuses, comments, reviews etc. available in them, application of automatic SA is on the rise. However, most of the research works on SA in natural language processing (NLP) are based on English language. Despite being the sixth most widely spoken language in the world, Bangla still does not have a large and standard dataset. Because of this, recent research works in Bangla have failed to produce re…
University of Denver（丹佛大学）；Eaton Senior Community（伊顿老年社区）
Social robots are becoming an integrated part of our daily life due to their ability to provide companionship and entertainment. A subfield of robotics, Socially Assistive Robotics (SAR), is particularly suitable for expanding these benefits into the healthcare setting because of its unique ability to provide cognitive, social, and emotional support. This paper presents our recent research on developing SAR by evaluating the ability of a life-like conversational social robot, called Ryan, to administer internet-delivered cognitive behavioral therapy (iCBT) to older adults with depression. For …
NepEMO: A Multi-Label Emotion and Sentiment Analysis on Nepali Reddit with Linguistic Insights and Temporal Trends
Department of Computer Science and Application（计算机科学与应用系）；Mechi Multiple Campus, Tribhuvan University（特里布文大学梅奇多校园）；School of Computer Science（计算机科学学院）；Queensland University of Technology（昆士兰理工大学）；Department of Electronics and Computer Engineering（电子与计算机工程系）；Advanced College of Engineering and Management, Tribhuvan University（特里布文大学先进工程与管理学院）；School of Engineering and Technology（工程与技术学院）
Chittagong University of Engineering and Technology（吉大港工程技术大学）
The amount of textual data generation has increased enormously due to the effortless access of the Internet and the evolution of various web 2.0 applications. These textual data productions resulted because of the people express their opinion, emotion or sentiment about any product or service in the form of tweets, Facebook post or status, blog write up, and reviews. Sentiment analysis deals with the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude toward a particular topic is positive,…
College of Information Science and Engineering, Ritsumeikan University（信息科学与工程学院，立命馆大学）；College of Computer Science and Technology, Zhejiang University（计算机科学与技术学院，浙江大学）
AI in Mental Health: Emotional and Sentiment Analysis of Large Language Models' Responses to Depression, Anxiety, and Stress Queries
University of Tehran（塔里斯坦大学）；University of South Carolina（南卡罗来纳大学）；University of West London（西伦敦大学）；Azad University（阿扎德大学）；Amirkabir University of Technology（阿米卡比尔技术大学）
Machine Learning Approaches for Mental Illness Detection on Social Media: A Systematic Review of Biases and Methodological Challenges
Khoury College of Computer Science, Northeastern University（东北大学科瑞计算机学院）；University of California, Berkeley（加州大学伯克利分校）；Center for Data Science, New York University（纽约大学数据科学中心）；School of Engineering and Applied Science, University of Pennsylvania（宾夕法尼亚大学工程与应用科学学院）；Georgia Institute of Technology（佐治亚理工学院）
Seoul National University（首尔大学）；Sungkyunkwan University（成均馆大学）；Yonsei University（延世大学）
Remembering an event is affected by personal emotional status. We examined the psychological status and personal factors; depression (Center for Epidemiological Studies - Depression, Radloff, 1977), present affective (Positive Affective and Negative Affective Schedule, Watson et al., 1988), life orient (Life Orient Test, Scheier & Carver, 1985), self-awareness (Core Self Evaluation Scale, Judge et al., 2003), and social factor (Social Support, Sarason et al., 1983) of undergraduate students (N=64) and got summaries of a story, Chronicle of a Death Foretold (Gabriel Garcia Marquez, 1981) from t…
Self-disclosed mental health diagnoses, which serve as ground truth annotations of mental health status in the absence of clinical measures, underpin the conclusions behind most computational studies of mental health language from the last decade. However, psychiatric conditions are dynamic; a prior depression diagnosis may no longer be indicative of an individual's mental health, either due to treatment or other mitigating factors. We ask: to what extent are self-disclosures of mental health diagnoses actually relevant over time? We analyze recent activity from individuals who disclosed a dep…
DepFlow: Disentangled Speech Generation to Mitigate Semantic Bias in Depression Detection
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；School of Electrical Engineering and Telecommunications, UNSW（新南威尔士大学电信工程学院）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；Center for AI in Medicine (C-AIM), Lee Kong Chian School of Medicine, NTU（人工智能医学中心（C-AIM）、李光耀医学院、南洋理工大学）
Evaluating Lexicon Incorporation for Depression Symptom Estimation
Normandie Univ, UNICAEN, ENSICAEN, CNRS, GREYC（诺曼底大学、卡昂大学、卡昂高等工程师学院、法国国家科学研究中心、格雷西研究所）；Institute of Computer Science, University of Tartu（塔尔图大学计算机科学研究所）
Mental well-being and social media have been closely related domains of study. In this research a novel model, AD prediction model, for anxious depression prediction in real-time tweets is proposed. This mixed anxiety-depressive disorder is a predominantly associated with erratic thought process, restlessness and sleeplessness. Based on the linguistic cues and user posting patterns, the feature set is defined using a 5-tuple vector <word, timing, frequency, sentiment, contrast>. An anxiety-related lexicon is built to detect the presence of anxiety indicators. Time and frequency of tweet is ana…
Multiple studies have demonstrated that behavior on internet-based social media platforms can be indicative of an individual's mental health status. The widespread availability of such data has spurred interest in mental health research from a computational lens. While previous research has raised concerns about possible biases in models produced from this data, no study has quantified how these biases actually manifest themselves with respect to different demographic groups, such as gender and racial/ethnic groups. Here, we analyze the fairness of depression classifiers trained on Twitter dat…
Indian Institute of Technology Patna（巴特那印度理工学院）；Wright State University（莱特州立大学）
The unprecedented growth of Internet users in recent years has resulted in an abundance of unstructured information in the form of social media text. A large percentage of this population is actively engaged in health social networks to share health-related information. In this paper, we address an important and timely topic by analyzing the users' sentiments and emotions w.r.t their medical conditions. Towards this, we examine users on popular medical forums (Patient.info,dailystrength.org), where they post on important topics such as asthma, allergy, depression, and anxiety. First, we provid…
Depression is a leading cause of disability worldwide, but is often under-diagnosed and under-treated. One of the tenets of cognitive-behavioral therapy (CBT) is that individuals who are depressed exhibit distorted modes of thinking, so-called cognitive distortions, which can negatively affect their emotions and motivation. Here, we show that individuals with a self-reported diagnosis of depression on social media express higher levels of distorted thinking than a random sample. Some types of distorted thinking were found to be more than twice as prevalent in our depressed cohort, in particula…
RWTH Aachen University（亚琛工业大学）；University of Amsterdam（阿姆斯特丹大学）
In recent years, there has been a surge of interest in research on automatic mental health detection (MHD) from social media data leveraging advances in natural language processing and machine learning techniques. While significant progress has been achieved in this interdisciplinary research area, the vast majority of work has treated MHD as a binary classification task. The multiclass classification setup is, however, essential if we are to uncover the subtle differences among the statistical patterns of language use associated with particular mental health conditions. Here, we report on exp…
INESC-ID Lisboa（里斯本INESC-ID研究所）；Instituto Superior Técnico（高等技术学院）；Universidade de Lisboa（里斯本大学）；Qntfy（Qntfy公司）；Universidade Europeia（欧洲大学）；Northeastern University（东北大学）
Mental illnesses adversely affect a significant proportion of the population worldwide. However, the methods traditionally used for estimating and characterizing the prevalence of mental health conditions are time-consuming and expensive. Consequently, best-available estimates concerning the prevalence of mental health conditions are often years out of date. Automated approaches to supplement these survey methods with broad, aggregated information derived from social media content provides a potential means for near real-time estimates at scale. These may, in turn, provide grist for supporting…
A Big Data Analytics System for Predicting Suicidal Ideation in Real-Time Based on Social Media Streaming Data
Dams and Water Resources Research Center, University of Mosul（摩苏尔大学大坝与水资源研究中心）；Yildiz Technical University（伊兹密尔技术大学）；Centre for Industrial Software, University of Southern Denmark（南丹麦大学工业软件中心）
Social media is a breeding ground for threat narratives and related conspiracy theories. In these, an outside group threatens the integrity of an inside group, leading to the emergence of sharply defined group identities: Insiders -- agents with whom the authors identify and Outsiders -- agents who threaten the insiders. Inferring the members of these groups constitutes a challenging new NLP task: (i) Information is distributed over many poorly-constructed posts; (ii) Threats and threat agents are highly contextual, with the same post potentially having multiple agents assigned to membership i…
University of Pennsylvania（宾夕法尼亚大学）；Nanyang Technological University（南洋理工大学）
A body of literature has demonstrated that users' mental health conditions, such as depression and anxiety, can be predicted from their social media language. There is still a gap in the scientific understanding of how psychological stress is expressed on social media. Stress is one of the primary underlying causes and correlates of chronic physical illnesses and mental health conditions. In this paper, we explore the language of psychological stress with a dataset of 601 social media users, who answered the Perceived Stress Scale questionnaire and also consented to share their Facebook and Tw…
