Paper PublishingDeep Learning Based Fusion Approach for Hate Speech Detection
Objective
The objective of this project is to present the principle of three types of text classification methods, ELMo, BERT and CNN, and applied them to hate speech detection, then the performance is improved by fusion from two perspectives: the fusion of the classification results of ELMo, BERT and CNN, and the fusion of the classification results of three CNN classifiers with different parameters.
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.
KEYWORDS: Hate Speech, Machine Learning, Bert
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
HARDWARE SPECIFICATIONS:
- Processor: I3/Intel
- Processor RAM: 4GB (min)
- Hard Disk: 128 GB
- Key Board: Standard Windows Keyboard
- Mouse: Two or Three Button Mouse
- Monitor: Any
SOFTWARE SPECIFICATIONS:
- Operating System: Windows 7+
- Server-side Script: Python 3.6+
- IDE: PyCharm
- Libraries Used: Pandas, Numpy,TensorFlow,Matplotlib.
Learning Outcomes
- About Python.
- About Pandas.
- About Numpy.
- About Machine Learning.
- About how to use the libraries.
- About Xampp.
- Project Development Skills:
- Problem analyzing skills.
- Problem solving skills.
- Creativity and imaginary skills.
- Programming skills.
- Deployment.
- Testing skills.
- Debugging skills.
- Project presentation skills.
- About PyCharm.
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