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Aspect-based sentiment analysis using deep networks and stochastic optimization

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Sentiment analysis, also known as opinion mining, is a computational study of unstructured textual information which is used to analyze a persons attitude from a piece of text. This paper… Click to show full abstract

Sentiment analysis, also known as opinion mining, is a computational study of unstructured textual information which is used to analyze a persons attitude from a piece of text. This paper proposes an efficient method for sentiment analysis by effectively combining three procedures: (a) creating the ontologies for extraction of semantic features (b) Word2vec for conversion of processed corpus (c) convolutional neural network (CNN) for opinion mining. For CNN parameter tuning, a multi-objective function is solved for nondominant Pareto front optimal values using particle swarm optimization. Experiments show that the proposed technique outperforms other state-of-the-art techniques while yielding 88.52%, 94.30%, 85.63% and 86.03% in accuracy, precision, recall and F -measure, respectively.

Keywords: based sentiment; sentiment; aspect based; optimization; sentiment analysis

Journal Title: Neural Computing and Applications
Year Published: 2019

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