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FinnSentiment – A Finnish Social Media Corpus for Sentiment Polarity Annotation
Sentiment analysis and opinion mining is an important task with obvious ...
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Depression Status Estimation by Deep Learning based Hybrid Multi-Modal Fusion Model
Preliminary detection of mild depression could immensely help in effecti...
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Adapting Deep Learning Methods for Mental Health Prediction on Social Media
Mental health poses a significant challenge for an individual's well-bei...
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Multi-task dialog act and sentiment recognition on Mastodon
Because of license restrictions, it often becomes impossible to strictly...
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Anxious Depression Prediction in Real-time Social Data
Mental well-being and social media have been closely related domains of ...
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The Effect of Pets on Happiness: A Large-scale Multi-Factor Analysis using Social Multimedia
From reducing stress and loneliness, to boosting productivity and overal...
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Transfer Learning Approach for Detecting Psychological Distress in Brexit Tweets
In 2016, United Kingdom (UK) citizens voted to leave the European Union ...
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A Novel Sentiment Analysis Engine for Preliminary Depression Status Estimation on Social Media
Text sentiment analysis for preliminary depression status estimation of users on social media is a widely exercised and feasible method, However, the immense variety of users accessing the social media websites and their ample mix of vocabularies makes it difficult for commonly applied deep learning-based classifiers to perform. To add to the situation, the lack of adaptability of traditional supervised machine learning could hurt at many levels. We propose a cloud-based smartphone application, with a deep learning-based backend to primarily perform depression detection on Twitter social media. The backend model consists of a RoBERTa based siamese sentence classifier that compares a given tweet (Query) with a labeled set of tweets with known sentiment ( Standard Corpus ). The standard corpus is varied over time with expert opinion so as to improve the model's reliability. A psychologist ( with the patient's permission ) could leverage the application to assess the patient's depression status prior to counseling, which provides better insight into the mental health status of a patient. In addition, to the same, the psychologist could be referred to cases of similar characteristics, which could in turn help in more effective treatment. We evaluate our backend model after fine-tuning it on a publicly available dataset. The find tuned model is made to predict depression on a large set of tweet samples with random noise factors. The model achieved pinnacle results, with a testing accuracy of 87.23
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