The wanton spread of hate speech on the internet brings great harm to society and families. It is urgent to establish and improve automatic detection and active avoidance mechanisms for hate speech. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. In other words, getting more affective features from other affective resources will significantly affect the performance of hate speech detection. In this paper, we propose a hate speech detection framework based on sentiment knowledge sharing. While extracting the affective features of the target sentence itself, we make better use of the sentiment features from external resources, and finally fuse features from different feature extraction units to detect hate speech. Experimental results on two public datasets demonstrate the effectiveness of our model.

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In accordance with the problem that the traditional experiment board of computer composition principle can only be used for functional realization and verification, which cannot satisfy the students' design and innovation, this paper proposes an improved experiment board method of computer composition principle. This method has used the complex programmable logic device (CPLD) chip to replace the original discrete small-scale integration chip; the Quartus II is used to complete the circuit description by drawing schematic diagram; in the meantime, the control signal board and the data path board are integrated, which form a complete computer system with the microprogrammed board, which can be used in the experiment teaching. The experimental test results show that the new experiment board based on CPLD has stable and reliable operation, which can realize functions consistent with the original experiment instrument, and it can be used as the EDA experiment board.