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Aspect-based sentiment analysis using deep networks and stochastic optimization
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Aspect-based sentiment analysis using deep networks and stochastic optimization
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Journal Article
Aspect-based sentiment analysis using deep networks and stochastic optimization
2020
Overview
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.
Publisher
Springer London,Springer Nature B.V
