Sentiment Analysis on the News to Improve Mental Health
This addresses mental health issues for news consumers by reducing anxiety and mood disturbance, though it is incremental as it applies existing sentiment analysis methods to a new application.
The paper tackled the problem of negativity bias in online news by developing a mobile app that filters and displays only uplifting stories, resulting in 85% of 1,300 users reporting improved mental health and a 4.9-star rating.
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.