Hot news mining and public opinion guidance analysis based on sentiment computing in network social media

The texts of social media event have feathers of massive-sparse, dynamic-heterogeneous, and obscure-vague, which increase the difficulty of event emotion computing. Aiming at the problem, we construct the dictionary supervised emotion computing model, which can be applied in hot news mining and public opinion guidance analysis based on sentiment computing in network social media. The text words and labels are used as the input of the models, and the profile distribution and emotion distribution of the texts, the word distribution of the profiles, and emotions are output by the models. In addition, the words with definite emotion are used as the constraint condition of the model to enhance the accuracy of text emotion calculation. Our proposed algorithm can express the emotion of the text by using the words and labels from labeled texts, and the emotion words value is calculated through a finite iteration of the network. We also make use of the word emotion in the basic word emotion dictionary to modify the network and then recompute the word emotion, which effectively overcomes the problem of emotion uncertainty of the traditional methods. Experiments show that the accuracy of our model is generally higher than that of ETM, MSTM, and SLTM. Therefore, our proposed method is effective and feasible.