Deep Learning Based Fusion Approach for Hate Speech Detection

IEEE BASE PAPER ABSTRACT:

In recent years, the increasing prevalence of hate speech in social media has been considered as a serious problem worldwide. Many governments and organizations have made significant investment in hate speech detection techniques, which have also attracted the attention of the scientific community. Although plenty of literature focusing on this issue is available, it remains difficult to assess the performances of each proposed method, as each has its own advantages and disadvantages. A general way to improve the overall results of classification by fusing the various classifiers results is a meaningful attempt. We first focus on several famous machine learning methods for text classification such as Embeddings from Language Models (ELMo), Bidirectional Encoder Representation from Transformers (BERT) and Convolutional Neural Network (CNN), and apply these methods to the data sets of the SemEval 2019 Task 5. We then adopt some fusion strategies to combine the classiers to improve the overall classification performance. The results show that the accuracy and F1-score of the classification are significantly improved.

PROJECT OUTPUT VIDEO:

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.

Hard Disk : 500 GB.

Monitor : 15’’ LED

Input Devices : Keyboard, Mouse

Ram : 4 GB

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

REFERENCE:

YANLING ZHOU, YANYAN YANG, HAN LIU, XIUFENG LIU, AND NICK SAVAGE, “Deep Learning Based Fusion Approach for Hate Speech Detection”, IEEE ACCESS, 2020.

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Deep Learning Based Fusion Approach for Hate Speech Detection