Assessing the Effectiveness of Affective Lexicons for Depression Classification

Postprin

Nowcasting user behaviour with social media and smart devices on a longitudinal basis: from macro- to micro-level modelling

The adoption of social media and smart devices by millions of users worldwide over the last decade has resulted in an unprecedented opportunity for NLP and social sciences. Users publish their thoughts and opinions on everyday issues through social media platforms, while they record their digital traces through their smart devices. Mining these rich resources offers new opportunities in sensing real-world events and indices (e.g., political preference, mental health indices) in a longitudinal fashion, either at the macro (population)-, or at the micro(user)-level. The current project aims at developing approaches to “nowcast" (predict the current state of) such indices at both levels of granularity. First, we build natural language resources for the static tasks of sentiment analysis, emotion disclosure and sarcasm detection over user-generated content. These are important for opinion monitoring on a large scale. Second, we propose a general approach that leverages textual data derived from generic social media streams to nowcast political indices at the macro-level. Third, we leverage temporally sensitive and asynchronous information to nowcast the political stance of social media users, at the micro-level using multiple kernel learning. We then focus further on the micro-level modelling, to account for heterogeneous data sources, such as information derived from users' smart phones, SMS and social media messages, to nowcast time-varying mental health indices of a small cohort of users on a longitudinal basis. Finally, we present the challenges faced when applying such micro-level approaches in a real-world setting and propose directions for future research

Toward explainable AI (XAI) for mental health detection based on language behavior

Advances in artificial intelligence (AI) in general and Natural Language Processing (NLP) in particular are paving the new way forward for the automated detection and prediction of mental health disorders among the population. Recent research in this area has prioritized predictive accuracy over model interpretability by relying on deep learning methods. However, prioritizing predictive accuracy over model interpretability can result in a lack of transparency in the decision-making process, which is critical in sensitive applications such as healthcare. There is thus a growing need for explainable AI (XAI) approaches to psychiatric diagnosis and prediction. The main aim of this work is to address a gap by conducting a systematic investigation of XAI approaches in the realm of automatic detection of mental disorders from language behavior leveraging textual data from social media. In pursuit of this aim, we perform extensive experiments to evaluate the balance between accuracy and interpretability across predictive mental health models. More specifically, we build BiLSTM models trained on a comprehensive set of human-interpretable features, encompassing syntactic complexity, lexical sophistication, readability, cohesion, stylistics, as well as topics and sentiment/emotions derived from lexicon-based dictionaries to capture multiple dimensions of language production. We conduct extensive feature ablation experiments to determine the most informative feature groups associated with specific mental health conditions. We juxtapose the performance of these models against a “black-box” domain-specific pretrained transformer adapted for mental health applications. To enhance the interpretability of the transformers models, we utilize a multi-task fusion learning framework infusing information from two relevant domains (emotion and personality traits). Moreover, we employ two distinct explanation techniques: the local interpretable model-agnostic explanations (LIME) method and a model-specific self-explaining method (AGRAD). These methods allow us to discern the specific categories of words that the information-infused models rely on when generating predictions. Our proposed approaches are evaluated on two public English benchmark datasets, subsuming five mental health conditions (attention-deficit/hyperactivity disorder, anxiety, bipolar disorder, depression and psychological stress)

Ruh sağlığı hastalıkları tanısında LIWC ve makine öğrenimi yaklaşımlarının incelenmesi

Machine learning methods are becoming increasingly popular in data analysis. In the field of mental healthcare, these methods provide support to mental disorder diagnosis. Pennebaker developed a dictionary-based text analysis program, and it is also used in mental health diagnosis. In this study, ML and Linguistic Inquiry Word Count (LIWC) studies conducted in the field of mental disorder diagnosis were examined. Researchers aim to integrate LIWC with machine learning to conduct more comprehensive studies. The objective of this study is to examine how combining ML and LIWC methods can detect mental disorder with a focus on comparative research. For this purpose, publications related to ML and LIWC in Google Scholar, Web of Science, Scopus, EBSCO, PubMed were examined. Studies utilizing machine learning and LIWC methods in mental health diagnosis were reviewed to establish an overview of the literature. A table summarizing 15 articles on integrating machine learning and LIWC for mental disorder identification was compiled. Subsequently, the working principles of machine learning and LIWC were examined, and research conducted in the field of mental disorder diagnosis was reviewed. Further research particularly those integrating or comparing these two methods needed to better understand machine learning and LIWC in mental disorder detection.Makine öğrenmesi yöntemleri veri analizi alanlarında giderek popülerlik kazanmaktadır. Bu yöntemler ruh sağlığı alanındaki tanı belirleme çalışmalarına da destek sağlamaktadır. İlk olarak, Pennebaker sözlük tabanlı bir metin analizi programı geliştirmiştir ve bu program ruh sağlığı teşhisinde de kullanılmaktadır. Bu çalışma kapsamında ruh sağlığı hastalıklar teşhisi alanında yapılmış olan makine öğrenmesi ve Linquistic Inquiry Word Count (LIWC) çalışmaları incelenmiştir. Günümüzde daha geniş araştırmalar yapabilmesi için LIWC ile makine öğrenimini birbirine entegre etmek amaçlanmaktadır. Bu çalışmanın amacı, makine öğrenmesi ve LIWC yöntemlerinin birbirine entegre edilmesinin ruh sağlığı hastalıklarının teşhisinde etkisinin araştırılmasıdır. Özellikle karşılaştırmalı araştırmalara odaklanılmıştır. Bu amaçla, makine öğrenmesi ve LIWC ile ilgili olan Google Scholar, SAGE journals, Web of Science, Scopus, EBSCO, PubMed kaynaklarındaki yayınlar incelenmiştir. Literatürdeki genel durumun ortaya konması amacıyla, ruh sağlığı hastalıkları tespitinde makine öğrenmesi ve LIWC yöntemlerinden yararlanan çalışmalar derlenmiştir. Son olarak makine öğrenimi ve LIWC’in çalışma prensipleri incelenip ruh sağlığı hastalıkları alanında yapılan araştırmalar ve bazı çalışmalar tablolaştırılmıştır. Bu çalışmanın, ruh sağlığı hastalıkları tespitinde makine öğrenimi ve Dilbilimsel Sorgulama Kelime Sayımını daha iyi anlamak için özellikle bu iki yöntemi entegre eden veya karşılaştıran daha fazla araştırmaya ihtiyaç olduğundan, araştırmacılara faydalı olabileceği umulmaktadır.Publisher's Versio

Sentiment Analysis in Digital Spaces: An Overview of Reviews

Sentiment analysis (SA) is commonly applied to digital textual data, revealing insight into opinions and feelings. Many systematic reviews have summarized existing work, but often overlook discussions of validity and scientific practices. Here, we present an overview of reviews, synthesizing 38 systematic reviews, containing 2,275 primary studies. We devise a bespoke quality assessment framework designed to assess the rigor and quality of systematic review methodologies and reporting standards. Our findings show diverse applications and methods, limited reporting rigor, and challenges over time. We discuss how future research and practitioners can address these issues and highlight their importance across numerous applications.Comment: 44 pages, 4 figures, 6 tables, 3 appendice