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MultiFeature fusion graph

MultiFeature fusion graph attention network for aspect-based sentiment analysis


Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task, which aims to predict the sentiment polarity of a given aspect in a sentence. Most of the approaches rely on syntactic and semantic parsing to derive textual insights, often overlooking how aspectual and contextual factors impact model performance. Alternatively, they focus on an in-depth study of the information in the dependency tree, thereby ignoring the importance of the constituent trees. In this work, we introduce a multifeature fusion graph attention network (MFF-GAT) model.

The model constructs syntactic, semantic and contextual channels, fusing dependent syntactic information and constituent syntactic information through a gating mechanism. The semantic graph is constructed based on self-attention, and the contextual graph is constructed based on point interaction information. In addition, this study uses the multi-head attention mechanism to interact with aspects and three features and capture aspect-related information. Our MFF-GAT model performs better on the ABSA task than other baseline models, according to experiments conducted on five public datasets.

Architecture of the sentiment classification model based on multi-graph attention networks(GAT). The framework comprises three modules: syntactic(Constituent/Dependency GAT), semantic(Semantic GAT), and contextual(Context GAT), which interact with aspect through multi-head attention mechanisms to ultimately predict sentiment polarity.

In this study, a multifeature fusion graph attention network model is suggested. This model effectively utilizes the syntactic information by fusing two syntactic features using a gating mechanism. Furthermore, self-attention is used to construct the semantic graph, word co-occurrence is used to create the context graph, and GAT is used to integrate syntactic structures, semantic representations, and contextual information into unified textual features for comprehensive information utilization.

Graph theory, network analysis, nodes and edges, directed graphs, undirected graphs, graph traversal, adjacency matrix, adjacency list, shortest path, Dijkstra’s algorithm, spanning tree, minimum spanning tree, Kruskal’s algorithm, Prim’s algorithm, graph coloring, bipartite graph, Eulerian path, Hamiltonian cycle, connected components

#GraphTheory, #NetworkAnalysis, #GraphTraversal, #DirectedGraph, #UndirectedGraph, #AdjacencyMatrix, #AdjacencyList, #ShortestPath, #DijkstraAlgorithm, #SpanningTree, #KruskalAlgorithm, #PrimAlgorithm, #GraphColoring, #BipartiteGraph, #EulerianPath, #HamiltonianCycle, #ConnectedComponents, #GraphIsomorphism, #TreeAndGraph, #GraphAlgorithms

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