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Found 19 papers, 4 papers with code
Multimodality for NLP-Centered Applications: Resources, Advances and Frontiers
With the development of multimodal systems and natural language generation techniques, the resurgence of multimodal datasets has attracted significant research interests, which aims to provide new information to enrich the representation of textual data.
Reliability Analysis of Psychological Concept Extraction and Classification in User-penned Text
The social NLP research community witness a recent surge in the computational advancements of mental health analysis to build responsible AI models for a complex interplay between language use and self-perception.
InterPrompt: Interpretable Prompting for Interrelated Interpersonal Risk Factors in Reddit Posts
Mental health professionals and clinicians have observed the upsurge of mental disorders due to Interpersonal Risk Factors (IRFs).
WellXplain: Wellness Concept Extraction and Classification in Reddit Posts for Mental Health Analysis
During the current mental health crisis, the importance of identifying potential indicators of mental issues from social media content has surged.
NBIAS: A Natural Language Processing Framework for Bias Identification in Text
Therefore, it is crucial to detect and remove these biases to ensure the fair and ethical use of data.
LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts
Low self-esteem and interpersonal needs (i. e., thwarted belongingness (TB) and perceived burdensomeness (PB)) have a major impact on depression and suicide attempts.
Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental Health
Amid ongoing health crisis, there is a growing necessity to discern possible signs of Wellness Dimensions (WD) manifested in self-narrated text.
LonXplain: Lonesomeness as a Consequence of Mental Disturbance in Reddit Posts
Social media is a potential source of information that infers latent mental states through Natural Language Processing (NLP).
An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Posts
With a surge in identifying suicidal risk and its severity in social media posts, we argue that a more consequential and explainable research is required for optimal impact on clinical psychology practice and personalized mental healthcare.
Towards Explainable and Safe Conversational Agents for Mental Health: A Survey
Virtual Mental Health Assistants (VMHAs) are seeing continual advancements to support the overburdened global healthcare system that gets 60 million primary care visits, and 6 million Emergency Room (ER) visits annually.
Multi-class Categorization of Reasons behind Mental Disturbance in Long Texts
We believe our work facilitates causal analysis of depression and suicide risk on social media data, and shows potential for application on other mental health conditions.
NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social Media
To bridge this gap, we posit two significant dimensions: (1) Causal analysis to illustrate a cause and effect relationship in the user generated text; (2) Perception mining to infer psychological perspectives of social effects on online users intentions.
Causal Categorization of Mental Health Posts using Transformers
With recent developments in digitization of clinical psychology, NLP research community has revolutionized the field of mental health detection on social media.
Explainable Causal Analysis of Mental Health on Social Media Data
In this task, we fine tune the classifiers and find explanations for multi-class causal categorization of mental illness on social media with LIME and Integrated Gradient (IG) methods.
An event detection technique using social media data
In this research work, the approach towards event detection from social media data is divided into three phases namely: Identifying sub-graphs in Microblog Word Co-occurrence Network (WCN) which provides important information about keyphrases; Identifying multiple events from social media data; and Ranking contextual information of event phrases.
Minimal Feature Analysis for Isolated Digit Recognition for varying encoding rates in noisy environments
In this research work, analysis of isolated digit recognition in the presence of different bit rates and at different noise levels has been performed.
CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts
We introduce a new dataset for Causal Analysis of Mental health issues in Social media posts (CAMS).
Data Augmentation for Mental Health Classification on Social Media
To handle this issue, we have studied the effect of data augmentation techniques on domain specific user generated text for mental health classification.
Quantifying the Suicidal Tendency on Social Media: A Survey
This potential manuscript elucidates the taxonomy of mental healthcare and highlights some recent attempts in examining the potential of quantifying suicidal tendency on social media data.
