A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19

• URL: http://arxiv.org/abs/2411.07163v1
• Date: Mon, 11 Nov 2024 17:41:54 GMT
• Title: A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19
• Abstract summary: We introduce a neurosymbolic method that integrates neural networks with symbolic knowledge sources. This method dynamically adapts to evolving language, outperforming purely data-driven models with an F1 score exceeding 92%. This study demonstrates the benefit of neurosymbolic methods in interpreting text in a dynamic environment for tasks such as health surveillance.
• Score: 18.07330996866408
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Monitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based, data-driven neural network-based approaches can miss newly relevant content due to the evolving nature of language in a dynamically evolving environment. Human-curated symbolic knowledge sources, such as lexicons for standard language and slang terms, can potentially elevate social media signals in evolving language. We introduce a neurosymbolic method that integrates neural networks with symbolic knowledge sources, enhancing the detection and interpretation of mental health-related tweets relevant to COVID-19. Our method was evaluated using a corpus of large datasets (approximately 12 billion tweets, 2.5 million subreddit data, and 700k news articles) and multiple knowledge graphs. This method dynamically adapts to evolving language, outperforming purely data-driven models with an F1 score exceeding 92\%. This approach also showed faster adaptation to new data and lower computational demands than fine-tuning pre-trained large language models (LLMs). This study demonstrates the benefit of neurosymbolic methods in interpreting text in a dynamic environment for tasks such as health surveillance.

Related papers

• Early Detection of Mental Health Issues Using Social Media Posts [0.0]
  Social media platforms, like Reddit, represent a rich source of user-generated content. We propose a multi-modal deep learning framework that integrates linguistic and temporal features for early detection of mental health crises.
  arXiv  Detail & Related papers  (2025-03-06T23:08:08Z)
• A Lesion-aware Edge-based Graph Neural Network for Predicting Language Ability in Patients with Post-stroke Aphasia [12.129896943547912]
  We propose a lesion-aware graph neural network (LEGNet) to predict language ability from resting-state fMRI (rs-fMRI) connectivity in patients with post-stroke aphasia. Our model integrates three components: an edge-based learning module that encodes functional connectivity between brain regions, a lesion encoding module, and a subgraph learning module.
  arXiv  Detail & Related papers  (2024-09-03T21:28:48Z)
• Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach [0.228438857884398]
  This study addresses the complex task of sentiment analysis on a dataset of 119,988 original tweets from Weibo using a Convolutional Neural Network (CNN) A CNN-based model was utilized, leveraging word embeddings for feature extraction, and trained to perform sentiment classification. The model achieved a macro-average F1-score of approximately 0.73 on the test set, showing balanced performance across positive, neutral, and negative sentiments.
  arXiv  Detail & Related papers  (2023-07-13T03:02:56Z)
• Probing Brain Context-Sensitivity with Masked-Attention Generation [87.31930367845125]
  We use GPT-2 transformers to generate word embeddings that capture a fixed amount of contextual information. We then tested whether these embeddings could predict fMRI brain activity in humans listening to naturalistic text.
  arXiv  Detail & Related papers  (2023-05-23T09:36:21Z)
• Mitigating Data Scarcity for Large Language Models [7.259279261659759]
  In recent years, pretrained neural language models (PNLMs) have taken the field of natural language processing by storm. Data scarcity are commonly found in specialized domains, such as medical, or in low-resource languages that are underexplored by AI research. In this dissertation, we focus on mitigating data scarcity using data augmentation and neural ensemble learning techniques.
  arXiv  Detail & Related papers  (2023-02-03T15:17:53Z)
• DBGDGM: Dynamic Brain Graph Deep Generative Model [63.23390833353625]
  Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as functional connectivity networks (FCNs), encode temporal relationships which can serve as useful biomarkers for understanding brain function and dysfunction. Previous works, however, ignore the temporal dynamics of the brain and focus on static graphs. We propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings.
  arXiv  Detail & Related papers  (2023-01-26T20:45:30Z)
• Leveraging Graph-based Cross-modal Information Fusion for Neural Sign Language Translation [46.825957917649795]
  Sign Language (SL), as the mother tongue of the deaf community, is a special visual language that most hearing people cannot understand. We propose a novel neural SLT model with multi-modal feature fusion based on the dynamic graph. We are the first to introduce graph neural networks, for fusing multi-modal information, into neural sign language translation models.
  arXiv  Detail & Related papers  (2022-11-01T15:26:22Z)
• Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps [75.84770193489639]
  We examine the impact of test loss, training corpus and model architecture on the prediction of functional Magnetic Resonance Imaging timecourses of participants listening to an audiobook. We find that untrained versions of each model already explain significant amount of signal in the brain by capturing similarity in brain responses across identical words. We suggest good practices for future studies aiming at explaining the human language system using neural language models.
  arXiv  Detail & Related papers  (2022-07-07T15:37:17Z)
• Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification [78.120927891455]
  State-of-the-art brain-to-text systems have achieved great success in decoding language directly from brain signals using neural networks. In this paper, we extend the problem to open vocabulary Electroencephalography(EEG)-To-Text Sequence-To-Sequence decoding and zero-shot sentence sentiment classification on natural reading tasks. Our model achieves a 40.1% BLEU-1 score on EEG-To-Text decoding and a 55.6% F1 score on zero-shot EEG-based ternary sentiment classification, which significantly outperforms supervised baselines.
  arXiv  Detail & Related papers  (2021-12-05T21:57:22Z)
• MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
  We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos. We release a new dataset, MEDICA, for mental health patient engagement detection.
  arXiv  Detail & Related papers  (2020-11-17T15:18:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.