The Anxieties Detection from Tweets about Distance Learning with Negative Sentiments

Yulia Dyulicheva

Abstract


The study of the mental health of society and the automatic detection of various types of disorders based on text analysis is one of the priority areas of e-health. The empirical investigation of the various anxieties and their causes in the pandemic period connecting with distance learning introduction based on an analysis of 71475 tweets is performed in the paper. The analysis of hashtags of tweets with negative sentiment and proposed patterns based on part of speech recognition allows us to extract regularities that describe sensations and causes of emotions associated with the appearance of the affective state. We created a dictionary of terms describing affective states based on the most frequent words with negative sentiment. Particular attention was paid to feature engineering using positive words from positive tweets and negative words from negative tweets, taking into account parts of speech. The proposed approach to feature engineering made it possible to reduce the dimension of the feature space while maintaining the quality of the classification (84%).   


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