Stress Detection using Natural Language Processing and Machine Learning over social Interactions

Abstract

Cyberspace is a vast soapbox for people to post anything that they witness in their day-to-day lives. Subsequently, it can be used as a very effective tool in detecting the stress levels of an individual based on the posts and comments shared by him/her on social networking platforms. We leverage large-scale datasets with tweets to successfully accomplish sentiment analysis with the aid of machine learning algorithms. We take the help of a capable deep learning pre-trained model called BERT to solve the problems which come with sentiment classification. The BERT model outperforms a lot of other well-known models for this job without any sophisticated architecture. We also adopted Latent Dirichlet Allocation which is an unsupervised machine learning method that’s skilled in 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. This helps us predict which topic is linked to the textual data. With the aid of the models suggested, we will be able to detect the emotion of users online. We are primarily working with Twitter data because Twitter is a website where people express their thoughts often. In conclusion, this proposal is for the well- being of one’s mental health. The results are evaluated using various metric at macro and micro level and indicate that the trained model detects the status of emotions bases on social interactions.

Social Media Posts as a Window into Mental Health: A Machine Learning Approach

“…Another study employed a combination of natural language processing and machine learning techniques to classify depression, anxiety, and stress in social media users, achieving an accuracy of 72.5% [16]. Other studies have employed decision tree algorithms [39] convolutional neural networks [40][41], and deep learning approaches [42] [43] to classify mental health conditions in social media data. Other studies have focused on predicting suicidal ideation on social media platforms.…”