Identification of Affective States Based on Automatic Analysis of Texts of Comments in Social Networks

Abstract

The paper considers the problem of classifying 3553 English-language comments from the social network Reddit based on various approaches to the vectorization of comment texts, including bag of words, TF-IDF, bigrams analysis based on pointwise mutual information (PMI) and sentiments, and the deep model BERT of the language representation. The use of a hybrid approach based on text vectorization using BERT and bigrams analysis have made it possible to improve the quality of comments classification up to 91%. Based on a cluster analysis of 1857 English-language comments describing anxiety, clusters were identified using BERT+k-means. The study proposes a hybrid approach based on the use of the LDA topic modeling method, the VADER sentiments analysis method, pointwise mutual information, and parts of speech analysis and permitting one to select bigrams and trigrams to describe clusters of comments. To visualize the extracted patterns in the form of trigrams, a knowledge graph was constructed that describes the subject area, and a comparison of the words of the selected target trigrams with the words of a custom dictionary describing various affective disorders has made it possible to determine the types of psychosocial stressors associated with affective disorders.

Hybrid Modeling of Anxiety Propagation in Response to Threat Stimuli Flow

“…Sentiment analysis using deep learning techniques has been widely explored during the last decade. Social networks are sources of threat stimulus flows, where large news portals spread their information, allowing comments to be left under their published news articles [55,56], and the world's largest websites such as Youtube.com, Facebook.com, and Wikipedia.org are based on the core principle that the content of these platforms is created and uploaded by their visitors [57,58]. Such interactivity allows users of social networks not only to learn something new but also to share their opinions on the topic of articles or information presented in another way.…”