Introducing a novel bi-functional method for exploiting sentiment in complex information networks
by Paraskevas Koukaras; Dimitrios Rousidis; Christos Tjortjis
International Journal of Metadata, Semantics and Ontologies (IJMSO), Vol. 15, No. 3, 2021

Abstract: This paper elaborates on multilayer Information Network (IN) modelling, utilising graph mining and machine learning. Although, Social Media (SM) INs may be modelled as homogeneous networks, real-world networks contain multi-typed entities, characterised by complex relations and interactions posing as heterogeneous INs. For mining data whilst retaining semantic context in such complex structures, we need better ways for handling multi-typed and interconnected data. This work conceives and performs several simulations on SM data. The first simulation models information, based on a bi-partite network schema. The second simulation utilises a star network schema, along with a graph database offering querying for graph metrics. The third simulation handles data from the previous simulations to generate a multilayer IN. The paper proposes a novel bi-functional method for sentiment extraction of user reviews/opinions across multiple SM platforms, considering the concepts of supervised/unsupervised learning and sentiment analysis.

Online publication date: Mon, 23-May-2022

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