SENTIMENTAL ANALYSIS: SEXUAL HARRASMENT DETECTION FOR SOCIAL MEDIA USING MACHINE LEARNING MEDIA
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SENTIMENTAL ANALYSIS: SEXUAL HARRASMENT DETECTION FOR SOCIAL MEDIA USING MACHINE LEARNING MEDIA
ABSTRACT
In this day and age, the usage of Social Media has increased
enormously in our daily lives. People like to share their experiences in
various social media accounts for their friends to see. Consequently, the
possibility and growth of cyber threats have increased as well. To reduce this
situation, we try to propose a system that can detect cyber-crimes such as
fraud, blackmail, spam, impersonation etc. from the social media network
Twitter. This type of study can help people to detect early threats and
possible criminal activity and the types of accounts to stay alert of in real
time thereby creating a more secure social media experience. Our main goal is
to compare various sentiment analysis approaches for detecting Sexual
Harrasment or threats from social media using three different machine learning
algorithms and form a comparison to determine which among the three gives out
the highest accuracy in order for us to decide how to detect cyber Sexual
Harrasment activity on the Internet and be alert of threats in both the real
and virtual world.
While social media offer
great communication opportunities, they also increase the vulnerability of
young people to threatening situations online. Recent studies report that cyberSexual
Harrasment constitutes a growing problem among youngsters. Successful
prevention depends on the adequate detection of potentially harmful messages
and the information overload on the Web requires intelligent systems to
identify potential risks automatically. The focus of this paper is on automatic
cyberSexual Harrasment detection in social media text by modelling posts
written by bullies, victims, and bystanders of online Sexual Harrasment. We
describe the collection and fine-grained annotation of a cyberSexual Harrasment
corpus for English and Dutch and perform a series of binary classification
experiments to determine the feasibility of automatic cyberSexual Harrasment
detection. We make use of linear support vector machines exploiting a rich
feature set and investigate which information sources contribute the most for
the task. Experiments on a hold-out test set reveal promising results for the
detection of cyberSexual Harrasment-related posts. After optimisation of the
hyperparameters, the classifier yields an F1 score of 64% and 61% for English
and Dutch respectively, and considerably outperforms baseline systems.
SENTIMENTAL ANALYSIS: SEXUAL HARRASMENT DETECTION FOR SOCIAL MEDIA USING MACHINE LEARNING MEDIA
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In this day and age, the usage of Social Media has increased enormously in our daily lives. People like to share their experiences in various social media accounts for their friends to see. Consequently, the possibility and growth of cyber threats have increased as well. To reduce this situation, we try to propose a system that can detect cyber-crimes such as fraud, blackmail, spam, impersonation etc. from the social media network Twitter. This type of study can help people to detect early threats and possible criminal activity and the types of accounts to stay alert of in real time thereby creating a more secure social media experience. Our main goal is to compare various sentiment analysis approaches for detecting Sexual Harrasment or threats from social media using three different machine learning algorithms and form a comparison to determine which among the three gives out the highest accuracy in order for us to decide how to detect cyber Sexual Harrasment activity on the Internet and be alert of threats in both the real and virtual world. While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberSexual Harrasment constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overload on the Web requires intelligent systems to identify potential risks automatic.. computer science project topics
SENTIMENTAL ANALYSIS: SEXUAL HARRASMENT DETECTION FOR SOCIAL MEDIA USING MACHINE LEARNING MEDIA