ABSTRACT:
With the rapid growth of social
networks and microblogging websites, communication
between people from different cultural and psychological backgrounds has become
more direct, resulting in more and more “cyber” conflicts between these people.
Consequently, hate speech is used more and more, to the point where it has
become a serious problem invading these open spaces. Hate speech refers to the
use of aggressive, violent or offensive language, targeting a specific group of
people sharing a common property, whether this property is their gender (i.e.,
sexism), their ethnic group or race (i.e., racism) or their believes and
religion. While most of the online social networks and microblogging
websites forbid the use of hate speech, the size of these networks and websites
makes it almost impossible to control all of their content. Therefore, arises the necessity to detect such speech automatically and
filter any content that presents hateful language or language inciting to
hatred. In this paper, we propose an approach to detect hate expressions on
Twitter. Our approach is based on unigrams and patterns that are automatically
collected from the training set. These patterns and unigrams are later used,
among others, as features to train a machine learning algorithm. Our
experiments on a test set composed of 2010 tweets show that our approach
reaches an accuracy equal to 87.4% on detecting whether a tweet is offensive or
not (binary classification), and an accuracy equal to 78.4% on detecting
whether a tweet is hateful, offensive, or clean (ternary classification).
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
HAJIME WATANABE,
MONDHER BOUAZIZI , AND TOMOAKI OHTSUKI, “Hate Speech
on Twitter: A Pragmatic Approach to Collect Hateful and Offensive Expressions
and Perform Hate Speech Detection”, IEEE Access, 2018