Opinion Mining for the detection of Hate Speech on Social Media
Date
2019-11
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Pulchowk Campus
Abstract
Since hate speech and offensive language is becoming a growing problem, with the
massive availability and popularity of opinion-rich resources such as social media,
online review sites Hate speech on social media has unfortunately become a common
occurrence largely due to advances in mobile computing and the internet. This thesis
work analyses the social media content to try and identify hate speech, and with the
application of various machine intelligence models, improving the detection rate with
higher accuracy in contrast to previous research works. Various deep learning
approaches such as Convolutional Neural Network (CNN) and Recurrent Neural
Network (RNN) are used in the proposed approach. With the application of sentiment
weighing, the opinion mining is carried out. The final model shows overall precision of
0.82, recall 0.78 and F1 score of 0.80. The test accuracy achieved in the dataset is 83%.
For the visual interpretation, ROC curve shows the distinction between the abusive or
offensive and non-offensive content.
Description
Since hate speech and offensive language is becoming a growing problem, with the
massive availability and popularity of opinion-rich resources such as social media,
online review sites Hate speech on social media has unfortunately become a common
occurrence largely due to advances in mobile computing and the internet.
Keywords
CNN, LSTM, hate, opinion