Abstract:
With the ever-increasing data on the internet especially regarding news,
everything is becoming data intensive politics, business, sports, government, and other myriad of domains have been scoured for data by big
companies over the last decade, World Economic forum has declared data a
new class of economic asset, like currency or gold which makes it a resource
to invest in. Text is a huge part of the data available online, this makes
NLP (Natural Language Processing) a desired technique and is needed to
face this mountain of unstructured data. News Websites publish thousands
of articles each day, and some of them do put out harmful or even fake
news on the internet (there has been a lot of work done regarding fake
news) and to monitor these huge chunks of data it is impossible to be done
by a single human or a team, and if we could do this in real time with
machines this could save us tremendous time and resources. The primary
purpose of our project is to develop a machine learning model that could
be given a set of articles and it can produce the Aspect level sentiments
of each article on a sentence base, we will have huge amount of data to be
processed and labeled at every aspect, this is a building block for further
implementation that can be an alert system, or a PR tool for analysis regarding an Entity.
