What are We Depressed about When We Talk about COVID19: Mental Health Analysis on Tweets Using Natural Language Processing
The outbreak of coronavirus disease 2019 (COVID-19) recently has affected human life to a great extent. Besides direct physical and economic threats, the pandemic also indirectly impact people's mental health conditions, which can be overwhelming but difficult to measure. The problem may come from various reasons such as unemployment status, stay-at-home policy, fear for the virus, and so forth. In this work, we focus on applying natural language processing (NLP) techniques to analyze tweets in terms of mental health. We trained deep models that classify each tweet into the following emotions: anger, anticipation, disgust, fear, joy, sadness, surprise and trust. We build the EmoCT (Emotion-Covid19-Tweet) dataset for the training purpose by manually labeling 1,000 English tweets. Furthermore, we propose and compare two methods to find out the reasons that are causing sadness and fear.
Code (1)
Similar Papers 제목 키워드 기반
“Politeness, you simpleton!” retorted [MASK]: Masked prediction of literary characters
What is the best way to learn embeddings for entities, and what can be learned from them? We consider this question for the case of literary characters. We address the highly challenging task of guessing, from a sentence…
Entity EmbeddingsSentenceSentiment Analysis - What are we talking about?
Gradual Classical Logic for Attributed Objects
There is knowledge. There is belief. And there is tacit agreement.' 'We may talk about objects. We may talk about attributes of the objects. Or we may talk both about objects and their attributes.' This work inspects tac…
AttributeRelationAbnormal Object Recognition: A Comprehensive Study
When describing images, humans tend not to talk about the obvious, but rather mention what they find interesting. We argue that abnormalities and deviations from typicalities are among the most important components that …
Anomaly DetectionObjectObject RecognitionMeasuring Issue Ownership using Word Embeddings
Sentiment and topic analysis are common methods used for social media monitoring. Essentially, these methods answers questions such as, "what is being talked about, regarding X", and "what do people feel, regarding X". I…
Document EmbeddingWord Embeddings