Word embedding technology plays an important role in text sentiment analysis, but the traditional word embedding technologies such as Word2Vec and GloVe (Global Vectors for word representation) will lead to the problem of single semantics. Aiming at the above problem, a text sentiment analysis model named Sentiment Lexicon Parallel-Embedding from Language Model (SLP-ELMo) based on sentiment lexicon and context language model named Embedding from Language Model (ELMo) was proposed. Firstly, the sentiment lexicon was used to filter the words in the sentence. Secondly, the filtered words were input into the character-level Convolutional Neural Network (char-CNN) to generate the character vector of each word. Then, the character vectors were input into ELMo model for training. In addition, the attention mechanism was added to the last layer of ELMo vector to train the word vectors better. Finally, the word vectors and ELMo vector were combined in parallel and input into the classifier for text sentiment classification. Compared with the existing models, the proposed model achieves higher accuracy on IMDB and SST-2 datasets, which validates the effectiveness of the model.

Existing research works on the influence of social networks mainly focus on the propagation of single-source information, and rarely consider the reverse form of propagation. Aiming at the problem of reverse influence maximization, the heat diffusion model was extended to the multi-source heat diffusion model, and a Pre-Selected Greedy Approximation (PSGA) algorithm was designed. In order to verify the validity of the algorithm, seven representative seed mining methods were selected, and the experiments were carried out on different kinds of social network datasets with the propagation revenue of reverse influence maximization, the running time of the algorithm and the degree of seed enrichment degree as evaluation indexes. The results show that the seeds selected by PSGA algorithm have stronger propagation ability, low intensity, and high stability performance, and have advantage in the early stage of propagation. It can be thought that PSGA algorithm can solve the problem of reverse influence maximization.