  In today's scenario, imagining a world without negativity is something very
unrealistic, as bad NEWS spreads more virally than good ones. Though it seems
impractical in real life, this could be implemented by building a system using
Machine Learning and Natural Language Processing techniques in identifying the
news datum with negative shade and filter them by taking only the news with
positive shade (good news) to the end user. In this work, around two lakhs
datum have been trained and tested using a combination of rule-based and data
driven approaches. VADER along with a filtration method has been used as an
annotating tool followed by statistical Machine Learning approach that have
used Document Term Matrix (representation) and Support Vector Machine
(classification). Deep Learning algorithms then came into picture to make this
system reliable (Doc2Vec) which finally ended up with Convolutional Neural
Network(CNN) that yielded better results than the other experimented modules.
It showed up a training accuracy of 96%, while a test accuracy of (internal and
external news datum) above 85% was obtained.
