Hybrid Feature based Prediction of Suicide Related Activity on Twitter

IEEE BASE PAPER ABSTRACT:

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. As long-range interpersonal communication destinations have gotten progressively normal, clients have embraced these locales to discuss strongly close to home points, among them their considerations about suicide. The tweets are significant for investigation since information shows up at a high recurrence and calculations that procedure them must do as such under extremely severe imperatives of capacity and time. Right now, we can separate Emoticons and Synonyms Feature and utilized n-gram model which is a mix of Unigram, Bigram, and Trigram with half breed word reference for score computation. This model utilizing the casual points to anticipate the earnestness of the posts using machine learning algorithms. In this research, we also compare different approaches like SVM, NB, and RF.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

Naïve Bayes.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.

Hard Disk : 500 GB.

Monitor : 15’’ LED

Input Devices : Keyboard, Mouse

Ram : 4 GB

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

REFERENCE:

Vidita Patel, Harshal Shah, Yassir Farooqui, “Hybrid Feature based Prediction of Suicide Related Activity on Twitter”, IEEE CONFERENCE, 2020.

Python IEEE Projects

Hybrid Feature based Prediction of Suicide Related Activity on Twitter