Abstract: The embodiment of the invention discloses a diversified recommendation method for news based on graph neural network and its device, which comprises the followings: word segmentation processing is performed on the target news text to obtain a word segmentation set, and vectorization processing is performed on the word segmentation set, so that each word segmentation in the word segmentation set has a word segmentation embedding vector; vectorization processing is performed on a target user group, so that each user in the target user group has a user embedding vector; the word segmentation embedding vector and the user embedding vector are input into the attention mechanism algorithm to obtain the news embedding vector; a graph neural network is constructed according to the user embedding vector and the news embedding vector, and the recommendation model is trained to obtain diversified recommendation results.

Abstract: A specific target-oriented social media tweet sentiment analysis method includes: preprocessing social media tweet data to obtain a target text and a specific target; passing the target text and the specific target through an embedding layer to obtain target text word vectors and obtain a specific target word vector, respectively; passing the target text word vectors through a self-attention structure to obtain a self-attention result; combining the self-attention result with the specific target word vector and passing through a cross-attention structure to obtain cross-attention results; concatenating the cross-attention results to obtain an attention representation matrix; and passing the attention representation matrix through a pooling layer, a fully connected layer and a softmax layer to obtain a sentiment tendency of the specific target.

Abstract: A specific target-oriented social media tweet sentiment analysis method includes: preprocessing social media tweet data to obtain a target text and a specific target; passing the target text and the specific target through an embedding layer to obtain target text word vectors and obtain a specific target word vector, respectively; passing the target text word vectors through a self-attention structure to obtain a self-attention result; combining the self-attention result with the specific target word vector and passing through a cross-attention structure to obtain cross-attention results; concatenating the cross-attention results to obtain an attention representation matrix; and passing the attention representation matrix through a pooling layer, a fully connected layer and a softmax layer to obtain a sentiment tendency of the specific target.

Abstract: A text sentiment analysis method based on multi-level graph pooling includes steps of: preprocessing a target text; taking collocate point mutual information between word nodes as an edge weight between the word nodes, and building a graph for each text independently; constructing a multi-level graph pooling model, of which a gated graph neural network layer transfers low-level information, a first graph self-attention pooling layer performs an initial graph pooling operation and uses a Readout function to extract low-level features, a second graph self-attention pooling layer performs a graph pooling operation again, performs a pruning update on the graph structure by calculating attention scores of nodes in the graph and uses a Readout function to extract high-level features; obtaining a multi-level final vector representation through a feature fusion function; and selecting a sentiment category corresponding to a maximum probability value as a final sentiment category output of the text.

Abstract: A social recommendation method based on a multi-feature heterogeneous graph neural network is provided and includes: extracting attribute information of users and topics to code; processing user coding information and topic coding information through a multi-layer perceptron to obtain initial feature vectors of the users and the topics; establishing a heterogeneous graph by taking the users and the topics as nodes; inputting the heterogeneous graph into a heterogeneous graph neural network to perform information transmission in combination with an attention mechanism, and updating the feature vectors; and performing similarity calculation on the feature vectors of the users, and selecting the user and the topic with the highest similarity with the feature vector of the user for recommendation. Social information can be mined more comprehensively, features of users and interested topics of the users can be deeply fused, and recommendation accuracy and user experience can be improved.

Abstract: A social recommendation method based on a multi-feature heterogeneous graph neural network is provided and includes: extracting attribute information of users and topics to code; processing user coding information and topic coding information through a multi-layer perceptron to obtain initial feature vectors of the users and the topics; establishing a heterogeneous graph by taking the users and the topics as nodes; inputting the heterogeneous graph into a heterogeneous graph neural network to perform information transmission in combination with an attention mechanism, and updating the feature vectors; and performing similarity calculation on the feature vectors of the users, and selecting the user and the topic with the highest similarity with the feature vector of the user for recommendation. Social information can be mined more comprehensively, features of users and interested topics of the users can be deeply fused, and recommendation accuracy and user experience can be improved.

Abstract: A text sentiment analysis method based on multi-level graph pooling includes steps of: preprocessing a target text; taking collocate point mutual information between word nodes as an edge weight between the word nodes, and building a graph for each text independently; constructing a multi-level graph pooling model, of which a gated graph neural network layer transfers low-level information, a first graph self-attention pooling layer performs an initial graph pooling operation and uses a Readout function to extract low-level features, a second graph self-attention pooling layer performs a graph pooling operation again, performs a pruning update on the graph structure by calculating attention scores of nodes in the graph and uses a Readout function to extract high-level features; obtaining a multi-level final vector representation through a feature fusion function; and selecting a sentiment category corresponding to a maximum probability value as a final sentiment category output of the text.