Qualitative Analysis Of Hostility Over Social Media Using A.I

Project Title

Qualitative Analysis Of Hostility Over Social Media Using A.I

Project Area of Specialization Artificial IntelligenceProject Summary

An analysis of a sentiment is a kind of insight which gives a huge leg up in shaping up a marketing or
security strategy that addresses prominent concerns and highlights points that are likely to draw customers
or audience towards your products or services. Human beings possess the ability to identify the emotions
from text, but they are limited, provided time and energy, but the machines are not. With machine learning,
media sentiment analysis is also able to provide knowledge regarding someone who has written negatively
about you or regarding any hate speech. Certain action can thus be taken according to those remarks from
the hate group. In the similar context, Blasphemy can be detected and reported to the authorities, eventually
making the society hatred free.

Project Objectives

The objectives are; to develop a system which scrapes textual data from social media, to process the
sentiments of textual data, to help organizations determine their service worth, to determine the next best
step for the organization, to implement a tracing operation on hateful elements

Project Implementation Method

Crawling data on a specific keyword from social media sites.
? Applying data integration and data mining techniques.
? Evaluating the sentiment by measuring the polarity of the data using machine learning and A.I approach.
? Providing statistical report on the data specified.
? Deploying the functions on website with cloud base.
? Monitoring user behaviors geographic wise.
The Equipment/Tools involved include
? Anaconda Jupyter Notebook
? Pycharm IDLE
? Cloud database

Benefits of the Project

Help Authorities to gain insight of public opinion.

Help in identifying hostile individuals

help in detection of communities of the said individuals using SNA.

collect evidence and present in the court.

Technical Details of Final Deliverable

Data scrapping using tweepy and scscrape. data preprocessing using NLTK. Data anntation using text blob and vader. sentiment prediction , traditional approach: Naive Bayes and logistic regression, deeplearning: RNN with LSTM. Data analyticsusing numpy, pandas , matplotlib, seaborn and fpdf. email module using SMTP library, geo fencing using folium, cyber profiling using Pandas, web dev using react and django.

final deliverable: Data analytics and evidence collection tool in the form of a website.

Final Deliverable of the Project Software SystemCore Industry SecurityOther Industries IT , Legal Core Technology Artificial Intelligence(AI)Other Technologies Cloud Infrastructure, OthersSustainable Development Goals Good Health and Well-Being for People, Industry, Innovation and Infrastructure, Peace and Justice Strong InstitutionsRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	73300
Proposal	Miscellaneous	3	150	450
Mid Year Report	Miscellaneous	1	300	300
Final Year Report	Miscellaneous	1	1000	1000
Data Analytics Report	Miscellaneous	3	250	750
Cyber Profiles	Miscellaneous	4	100	400
Behavioral Logs	Miscellaneous	4	100	400
Server	Equipment	1	70000	70000