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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Human factors are the main cause of mine accidents. With the aid of the Apriori algorithm, this paper establishes a new correlation analysis model for human factors in mine accidents. The model can predict the failure probability of human factors. Next, an evaluation index system for human reliability of mine operators was constructed. On this basis, fuzzy comprehensive evaluation (FCE) and analytic hierarchy process (AHP) were combined with neural network (NN) to evaluate human reliability of mine operators with stable performance. Meanwhile, principal component analysis (PCA) was combined with NN to evaluate human reliability of mine operators with unstable performance. Compared with the traditional evaluation methods, the proposed model reduces the dimension of input vector set, tolerates incomplete data samples, and achieves a high prediction accuracy.