The document summarizes a student project on hate speech detection using natural language processing and machine learning. The project aims to recognize hateful and discriminatory tweets and comments across social media platforms. It will do this by analyzing the vast amount of text data online to extract important insights, with the goal of reducing cyberbullying and improving speech data for future NLP and ML technologies.

1. Hate speech Detection Using NLP & Machine learning
DETAILS OF THE PROJECT
FARAZUL HODA(RA1811003020603)
BIBEK KUMAR GHOSH(RA1811003020587)
BISHAL RAJ MAJUMDER(RA1811003020586)
SUPERVISOR DETAILS
Ms.Preethi Jemima
Asst. Professor
SRM Institute of Science and Technology,
Ramapuram Campus, Chennai-89
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
Batch No: J-10
13-Feb-22
Department of Computer Science and
Engineering
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2. OBJECTIVE(1 slide)
Nowadays, as we all well know, the influence of social media and social networks
plays a huge role in our society regardless of the country in which we live in. More
than 8,000 tweets per second are posted every day which amount to something like
260 billion tweets per year. This astonishing mass of text encloses an invaluable
amount of information from which important insight could be extracted. The process
of analyzing text information belongs to the area of Natural Language Processing
(NPL).
The goal of this project is to recognize hateful and discriminatory tweets and
comments across multiple social media platforms.
13-Feb-22
Department of Computer Science and
Engineering
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3. SCOPE(1 slide)
• In this day and age of full internet implementation across all of the fields
imaginable, a lot of things are underway that enable everyone to be able to
put forward their opinions about everyone, any time, anywhere. And even
though that is a very good feature provided by the internet, we all know that
it is still only a tool and the use of every tool is determined by the one who
uses the tool.
• This is why this interesting freeing feature has enabled a lot of miscreants to
bully people, called as cyber-bullying. This is not a new problem and there
are solutions to this - a common example would be moderators on social
media websites like Discord and Reddit, but that still depends on people
and the sole purpose of technology is to reduce dependency on people
because people are limited.
13-Feb-22
Department of Computer Science and
Engineering
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4. • The function of this project is to detect hate-speech, i.e., when and where
someone is being bullied. If this project is then implemented on the same
social media websites as a substitute for moderators, the prevention of
bullying can be grown majorly and across more social media websites than
currently being moderated.
• This will help not only in fighting cyberbullying, but also in adding more
speech data for the numerous technologies that will follow in the coming
years related to NLP and ML.
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5. ABSTRACT(1 slide)
• In order to develop a fairly intuitive, though not exhaustive,
analysis on the hate speech detection, different types of
datasets have been exploited.
• astonishing mass of text encloses an invaluable amount of
information from which important insight could be extracted.
The process of analyzing text information belongs to the area
of Natural Language Processing (NLP).
13-Feb-22
Department of Computer Science and
Engineering
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6. REFERENCES
• Base paper to be listed first
• Follow the recent IEEE paper reference format
13-Feb-22
Department of Computer Science and
Engineering
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