Emotional sentiment analysis of social media content for mental health safety

Text sentiment analysis is mostly used for the assessment of author’s mood depending on context. The purpose to discover exactness underlying emotion in a given situation. It has been applied various fields, including stock market predictions, social media data product evaluations, psychology, judiciary, forecasting, illness prediction, agriculture, and more. Many researchers have worked these topics generated important insights. These outcomes are useful field because they (outcomes) help people comprehend general summary quickly. Additionally, aids limiting harmful effects some posts sites such as Facebook Twitter. For reasons more, we proposing an approach filter content that could be emotionally user, through getting networks content; that, Twitter API get user posts, then, natural understanding language tool extract classify emotions into five basic emotional categories—Joy, sadness, anger, fear, disgust—into array emotions; after defined perfect from over 450 words English language. main this comprehensive research article examine proposed solution conducted improve quality displayed users emotionally.