Sentiment analysis and affective computing for depression monitoring

  title={Sentiment analysis and affective computing for depression monitoring},
  author={Chiara Zucco and Barbara Calabrese and Mario Cannataro},
  journal={2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
Depression is one of the most common and disabling mental disorders that has a relevant impact on society. Semiautomatic and/or automatic health monitoring systems could be crucial and important to improve depression detection and follow-up. Sentiment Analysis refers to the use of natural language processing and text mining methodologies aiming to identify opinion or sentiment. Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and… 

Figures and Tables from this paper

Hybrid approach to detecting symptoms of depression in social media entries
This study presents an innovative approach to the depression screening problem by applying Collgram analysis, which is a known effective method of obtaining linguistic information from texts, and creates a hybrid model achieving a diagnostic accuracy of 71%.
An hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM
This study explores the possibility of predicting a user’s mental condition by classifying the depressive from non-depressive ones using Twitter data using a hybrid of two deep learning architectures, Convolutional Neural Network (CNN) and bi-directional Long Short-Term Memory (biLSTM).
Empirical Investigation for Predicting Depression from Different Machine Learning Based Voice Recognition Techniques
Different process applied in different machine learning algorithms in recognizing voice signals will be used for scrutinizing the techniques for detecting depression levels in future and make a blooming change in the youth's life and solve the social unethical issues in hand.
Affective Computing and Emotion-Sensing Technology for Emotion Recognition in Mood Disorders
Emotions are species-typical patterns and can be a window to describe the human mind in action. Understanding emotion can provide invaluable insights into various mood disorders, including
Sentiment Analysis for Depression Based on Social Media Post by Using Natural Language Processing
In this model an algorithm that can examine Tweets Expressing self-assessed negative features by analyzing linguistic markers in social media posts will be designed, which provides report of person’s depression symptoms.
A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection
Cases of mental health issues are increasing continuously and have sped up due to COVID-19. There are high chances of developing mental health issues such as depression, anxiety, schizophrenia, and
DEPAC: a Corpus for Depression and Anxiety Detection from Speech
A novel mental distress analysis audio dataset DEPAC is introduced, labelled based on established thresholds on depression and anxiety standard screening tools, and a feature set consisting of hand-curated acoustic and linguistic features are presented, found effective in identifying signs of mental illnesses in human speech.
Detecting the magnitude of depression in Twitter users using sentiment analysis
  • Jini Jojo Stephen, P. P.
  • Computer Science, Psychology
    International Journal of Electrical and Computer Engineering (IJECE)
  • 2019
An efficient method that can detect the level of depression in Twitter users is proposed and sentiment scores calculated can be combined with different emotions to provide a better method to calculate depression scores.
Stress detection using natural language processing and machine learning over social interactions
This paper uses large-scale datasets with tweets to accomplish sentiment analysis with the aid of machine learning algorithms and a deep learning model, BERT for sentiment classification and adopted Latent Dirichlet Allocation which is an unsupervised machine learning method for scanning a group of documents, recognizing the word and phrase patterns within them, and gathering word groups and alike expressions that most precisely illustrate a set of documents.
Explainable Sentiment Analysis with Applications in Medicine
This work presents a critical review of explainable sentiment analysis models and discussed the insight of applying explainably sentiment analysis in the medical field.


Sentiment Analysis and Affective Computing: Methods and Applications
An introductory overview of affective computing and sentiment analysis is provided, through the discussion of the main processing techniques and applications, to obtain a more accurate and reliable detection of emotions and feelings for applications in the life sciences.
Speech vs. text: A comparative analysis of features for depression detection systems
It is found that a combination of features drawn from both speech and text lead to the best system performance, and a system built on text-based features compares to a speech-based system.
Book Review: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions by Bing Liu
This comprehensive introduction to sentiment analysis takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions.
Identifying depressive users in Twitter using multimodal analysis
A novel method for identifying the users with depressive moods by analyzing their daily tweets by exploiting all media types of tweets, i.e., images and emoticons as well as texts, and it is effective in finding depressive Moods for users.
Multimodal assistive technologies for depression diagnosis and monitoring
The proposition that the auditory and visual human communication complement each other, which is well-known in auditory-visual speech processing, is exploited and the proposed framework’s effectiveness in depression analysis is shown.
Predicting Depression via Social Media
It is found that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement.
Towards Automatically Classifying Depressive Symptoms from Twitter Data for Population Health
B baseline approaches for automatically encoding Twitter data with granular depressive symptoms associated with major depressive disorder are demonstrated, and the most accurate classifiers could predict classes with high-to-moderate F1-score performances for no evidence of depression, evidence of Depression, and depressive symptoms.
Computational challenges for sentiment analysis in life sciences
A brief overview of some concrete examples of applying sentiment analysis to social networks for healthcare purposes is given, the current type of tools existing for sentiment analysis are presented, and the challenges involved are summarized focusing on the role of high performance computing.
Predicting Depression Levels Using Social Media Posts
This research investigates how SNS user's posts can help classify users according to mental health levels and proposes a system that uses SNS as a source of data and screening tool to classify the user using artificial intelligence according to the UGC on SNS.
Sentiment analysis algorithms and applications: A survey