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Neural Computing and Applications

Erschienen in:

12.03.2019 | Original Article

Aspect-based sentiment analysis using deep networks and stochastic optimization

verfasst von: Ravindra Kumar, Husanbir Singh Pannu, Avleen Kaur Malhi

Erschienen in: Neural Computing and Applications | Ausgabe 8/2020

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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.

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Titel: Aspect-based sentiment analysis using deep networks and stochastic optimization
verfasst von: Ravindra Kumar
Husanbir Singh Pannu
Avleen Kaur Malhi
Publikationsdatum: 12.03.2019
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 8/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04105-z

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