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.
翻译:互联网的普及造就了振兴的数字媒体。通过点击驱动的货币化,记者们调整了其内容的优先次序,以适应高度竞争性的在线新闻氛围。由此产生的负面偏向是有害的,可能导致焦虑和情绪干扰。我们利用了经过各种数据集培训的4种情绪分析模型的管道 — — 使用序列模型、LSTM模型、BERT模型和SVM模型。合并后,应用程序,即移动应用程序,仅仅展示了振奋和鼓舞人心的故事,供用户阅读。结果已经取得了成功 — — 1,300名用户将应用程序评为4.9颗恒星,85%的用户报告使用它改善了心理健康。