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

• Published in: Automation and remote control Vol. 83; no. 12; pp. 1877 - 1885
• Main Author: Dyulicheva, Yu. Yu
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.12.2022; Springer Nature B.V
• Subjects: Affect (Psychology); CAE) and Design; Calculus of Variations and Optimal Control; Optimization; Classification; Cluster analysis; Computer-Aided Engineering (CAD; Control; Disorders; English language; Knowledge representation; Mathematics; Mathematics and Statistics; Mechanical Engineering; Mechatronics; Robotics; Social networks; Systems Theory; Texts; Thematic Issue; Words (language); sentiment analysis; mental health; LDA; BERT; BoW; VADER; knowledge graph; bigram; TF–IDF
• DOI: 10.1134/S00051179220120025
• ISSN: 0005-1179

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.