Detection of Stress Based on Social Media Blogs using ML (original) (raw)

Discovering Stress Based on Social Interaction on Social Networking Sites

Stress is essentially humans' response to various types of desires or threats. This response, when working properly, can help us to stay focused, energized and intellectually active, but if it is out of proportion, it can certainly be harmful leading to depression, anxiety, hypertension and a host of threatening disorders. Cyberspace is a huge soap box for people to post anything and everything that they experience in their day-to-day lives. Subsequently, it can be used as a very effective tool in determining the stress levels of an individual based on the posts and status updates shared by him/her. This is a proposal for a website which takes the Twitter username of the subject as an input, scans and analyses the subject's profile by performing Sentiment Analysis and gives out results. These results suggest the overall stress levels of the subject and give an overview of his/her mental and emotional state. The tool used for analysis of the social media account is Rapidminer. Rapid miner is an environment for various data mining and machine learning procedures with a very effective and simple GUI.

Detecting Stress Based on Social Networking Interactions

International journal of innovative technology and exploring engineering, 2019

Stress is a kind of demand to respond to any in your body's manner. It can be based on experiences that are both good and bad. Psychological stress threatens the health of individuals. People are used to exchanging their schedule and daily operations with colleagues on social media platforms with the reputation of a social media network, creating it possible to hold online social network information for stress detection. For a variety of applications data mining methods are used. Data mining plays a significant role in the detection of stress in sector. We proposed a new model in this article to detect stress. Initially, in this model, discover a correlation between stress states of user and effective public interactions. This describes a set of textual, visual and social characteristics related to stress from different elements and proposes a new hybrid model coupled with Convolutional Neural Network (CNN) to efficiently hold tweet content and data on social interaction to detect stress. The suggested model can enhance the detection efficiency by 97.8 percent, which is quicker than the current scheme, from the experimental outcomes.

What Does Social Media Say about Your Stress?

2016

With the rise of social media such as Twitter, people are more willing to convey their stressful life events via these platforms. In a sense, it is feasible to detect stress from social media data for proactive health care. In psychology, stress is composed of stressor and stress level, where stressor further comprises of stressor event and subject. By far, little attention has been paid to estimate exact stressor and stress level from social media data, due to the following challenges: 1) stressor subject identification, 2) stressor event detection, and 3) data collection and representation. To address these problems, we devise a comprehensive scheme to measure a user's stress level from his/her social media data. In particular, we first build a benchmark dataset and extract a rich set of stress-oriented features. We then propose a novel hybrid multi-task model to detect the stressor event and subject, which is capable of modeling the relatedness among stressor events as well a...

Psychological Stress Detection from Social Media Data using a Novel Hybrid Model

International Journal of Intelligent Systems and Applications in Engineering, 2018

One of the mental threat for individual's health identified is Psychological stress from social media data. Hence, necessity is to predict and manage stress before it turns into a serious problem. However, Conventional stress detection methods exist, that rely on psychological scales & physiological devices that need full of victims participation which is time-consuming, complex and expensive. With the trending growth of social networks, people are addicted towards sharing personal moods via social media platforms to influence other users, leading to stressfulness. The developed novel hybrid model Psychological Stress Detection (PSD), automatically detect the victims's psychological stress from social media data. It comprises of three (3) modules Probabilistic Naïve Bayes Classifier, Visual (Hue, Saturation, Value) and Social, to leverage text, image post and social interaction information; we defined set of stress-related textual 'F = {f1, f2, f3, f4}', visual 'vF = {vf1, vf2}', and social features 'sf' to predict stress from social media content. Experimental results show the proposed PSD model improves the detection process when compared to TensiStrength and Teenchat framework, PSD achieves 95% of Precision rate. PSD model will assist in developing stress detection tools for mental health agencies.

Machine Learning Driven Mental Stress Detection on Reddit Posts Using Natural Language Processing

Human-Centric Intelligent Systems

People’s mental conditions are often reflected in their social media activity due to the internet's anonymity. Psychiatric issues are often detected through such activities and can be addressed in their early stages, potentially preventing the consequences of unattended mental disorders like depression and anxiety. In this paper, the authors have implemented machine learning models and used various embedding techniques to classify posts from the famous social media blog site Reddit as stressful and non-stressful. The dataset used contains user posts that can be analyzed to detect patterns in the social media activity of those diagnosed with mental disorders. This paper uses different NLP (Natural Language Processing) tools such as ELMo (Embeddings from Language Models) word embeddings, BERT (Bidirectional Encoder Representations from Transformers) tokenizers, and BoW (Bag of Words) approach to create word/sentence data that can be fed to machine learning models. The results of e...

Stress Level Assessment of an Individual using Neural Networks based on Tweets

2018

Stress in workplace is deteriorating both physical and mental health of an individual. It also makes the individual less productive and less efficient in work. It is necessary to assess the stress level before coping with it. There are so many methodologies that are used to detect stress. Inspired by psychological research stating that people feel more comfortable to express feelings on social media than through verbal communication, we are proposing a simple model to assess the stress level of an individual which can prevent psychological complications. In this paper, we propose a system similar to twitter that analyzes the stress level of an individual which can be viewed by the admin. Main emphasis is laid on stress level detection using Neural Networks and Semantic Similarity. Dataset, consisting of the stressed words is trained. Then, we find the similarity between tweet content and the trained dataset using Leacock and Chodorow's (LCH) semantic similarity between words. Results show the stress level of each individual based on his/her tweet/s.

Studying the Influence of Toxicity and Emotion Features for Stress Detection on Social Media

European Conference on Social Media

It is crucial to detect and manage stress as early as possible before it becomes a severe mental and physical health problem. Some authors even introduce stress as a “silent killer” to emphasize the significance of early stress management. Traumatic global events such as COVID-19 have amplified stress throughout online communities and it is quite common to see that social media users often vent about their problems or situations online. The ability to detect a person's stress from their posts on social media platforms like Reddit or Twitter in a timely manner can help early stress management and consequently counters mental health conditions. In order to detect stress from social media posts, we must obtain the characteristics that signal a user's stress. Which motivates us to study how salient features influence stress detection. On social media, text-based methods of communication predominantly overtake verbal forms, which makes these platforms a convenient rich medium wit...

Psychological Analysis Using Social Media Data

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Mental Stress is an important aspect of our life that is given the least importance. We tend to ignore the fact that we need to be emotionally stable along with physical stability. To keep your mental state sound, we proposed this system where the psychological state of a person is being predicted. One such place where a person comes up and shares his/her thoughts, through texts is on social media with their friends. To detect such a state, we made use of NLP techniques accompanied by a reliable scale, the Perceived Stress Scale (PSS) developed by Cohen, Kamarck and Mermelstein. The huge texts were cleaned using text processing methods. In Machine Learning, there are many ways for sentimental analysis such: decision-based systems, Bayesian classifiers, support vector machines, neural networks and sample-based methods. We have performed sentimental analysis and in order to give the severity of the condition we made use of the Perceived Stress Scale (PSS). The model will be predicting whether the given text indicates stress or not and further classifies it as low, medium or high-level stress.

Depression and anxiety detection from blog posts data

2018

Depression and anxiety affect the life of many individuals and if the diagnosis is not stated in time it could lead to considerable health decline and even suicide. Nowadays, mental health specialists, as well as data scientists, work towards analyzing social media sources and, in particular, publicly available text messages and blogs to identify depressed people and provide them with necessary treatment and support. In this work, we adopt an experimental data collection approach to gather a corpus of blog posts from clinical and control subjects. Ill people are considered as clinical subjects while control subjects refer to healthy individuals. We inspect the latent topics found in collected data to analyze the blog’ content according to themes covered by blog authors. We experiment with various text encoding techniques such as Bag-of-Words (BOW), Term Frequency-Inverse Document Frequency (TFIDF) and topic model’s features. We apply Support Vector Machines (SVM) and Convolutional N...

IJERT-An Efficient Identification of Mental Stress by Utilizing Social Network

International Journal of Engineering Research and Technology (IJERT), 2020

https://www.ijert.org/an-efficient-identification-of-mental-stress-by-utilizing-social-network https://www.ijert.org/research/an-efficient-identification-of-mental-stress-by-utilizing-social-network-IJERTCONV8IS08006.pdf The growth of social network leads to more advantages as well as problematic issues. Now days the symptoms and result of this metal stress was analyzed passively and delayed clinical intervention. To overcome this issue, an early stage of identifying online social behavior through analysis in efficient way is proposed. It is challenging to detect SNMSs because the mental status cannot be directly observed from online social activity logs. Through social networks n number of users is communicating in the form of text can't be analyzed manually to identify the stage of particular user. Manual way of analyzing user chat history will leads to privacy issues therefore this issue has been overcome through Machine Learning (ML). In ML, Natural language processing (NLP) is implemented that analysis the text commented and understand the meaning of the comment. By this analysis a system will identify whether the person is in normal, good or bad (depression) situation without affecting their privacy and confidentiality of data. Therefore, our system will read each word in a command and analysis its meaning and monitor particular person for a specific period and identify whether the person is in good or in depression situation. The person situation is not good then intimation will be send to respective person's relative mail hence this leads to saving a person life from unexpected event occurrence.