Sentiment Analysis on the News to Improve Mental Health
The popularization of the internet created a revitalized digital media. With monetization driven by clicks, journalists have reprioritized their content for the highly competitive atmosphere of online news. The resulting negativity bias is harmful and can lead to anxiety and mood disturbance. We utilized a pipeline of 4 sentiment analysis models trained on various datasets - using Sequential, LSTM, BERT, and SVM models. When combined, the application, a mobile app, solely displays uplifting and inspiring stories for users to read. Results have been successful - 1,300 users rate the app at 4.9 stars, and 85% report improved mental health by using it.
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Methods
Adam •
Attention Dropout •
BERT •
Dense Connections •
Dropout •
GELU •
Layer Normalization •
Linear Layer •
Linear Warmup With Linear Decay •
LSTM •
Multi-Head Attention •
Residual Connection •
Scaled Dot-Product Attention •
Sigmoid Activation •
Softmax •
SVM •
Tanh Activation •
Weight Decay •
WordPiece
