Hybrid Feature based Prediction of Suicide Related Activity on Twitter

Description

Suicide is a disturbing general medical issue and increasing fatal every year around the world. This work naturally removed casual inactive subjects from online web base life twitter and communicating self-destructive ideations. We can separate Emoticons and Synonyms Feature and utilized engram model which is a mix of Unigram, Bigram, and Trigram with half breed word reference for score computation. Use different approach like SVM, Logistic regression, and RF. Suicide is a disturbing general medical issue and increasing fatal every year around the world. This work naturally removed casual inactive subjects from online web based life twitter and communicating self-destructive ideations. Right off the bat emotionally assessed the idle points and afterward comprehensively contrasted them with chance variables proposed by space specialists.”