Opinion Discrimination Using Complex Network Features
Topological and dynamic features of complex networks have proven in recent years to be suitable for capturing text characteristics, with various applications in natural language processing. In this article we show that texts with positive and negative opinions can be distinguished from each other when represented as complex networks. The distinction was possible with the use of several metrics, including degrees, clustering coefficient, shortest paths, global efficiency, closeness and accessibility. The multidimensional dataset was projected into a 2-dimensional space with the principal component analysis. The distinction was quantified using machine learning algorithms, which allowed a recall of 84.4% in the automatic discrimination for the negative opinions, even without attempts to optimize the pattern recognition process.
KeywordsComplex Network Linear Discriminant Analysis Natural Language Processing Machine Translation Positive Opinion
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