A hybrid semantic knowledgebase-machine learning approach for opinion mining

Alfrjani, R., Osman, T. ORCID: 0000-0001-8781-2658 and Cosma, G. ORCID: 0000-0002-4663-6907, 2019. A hybrid semantic knowledgebase-machine learning approach for opinion mining. Data & Knowledge Engineering. ISSN 0169-023X

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Opinion mining tools enable users to efficiently process a large number of online 10 reviews in order to determine the underlying opinions. This paper presents a Hybrid Semantic Knowledgebase-Machine Learning approach for mining opinions at the domain feature level and classifying the overall opinion on a multi-point scale. The proposed approach benefits from the advantages of deploying a novel Semantic Knowledgebase approach to analyse a collection of reviews at the domain feature level 15 and produce a set of structured information that associates the expressed opinions with specific domain features. The information in the knowledgebase is further supplemented with domain-relevant facts sourced from public Semantic datasets, and the enriched semantically-tagged information is then used to infer valuable semantic information about the domain as well as the expressed opinions on the domain features 20 by summarising the overall opinions about the domain across multiple reviews, and by averaging the overall opinions about other cinematic features. The retrieved semantic information represents a valuable resource for modelling a machine learning classifier to predict the numerical rating of each review. Experimental evaluation revealed that the proposed Hybrid Semantic Knowledgebase-Machine Learning approach improved 25 the precision and recall of the extracted domain features, and hence proved suitable for producing an enriched dataset of semantic features that resulted in higher classification accuracy.

Item Type: Journal article
Publication Title: Data & Knowledge Engineering
Creators: Alfrjani, R., Osman, T. and Cosma, G.
Publisher: Elsevier
Date: 20 May 2019
ISSN: 0169-023X
S0169023X18300399Publisher Item Identifier
Rights: Open access. Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Divisions: Schools > School of Science and Technology
Record created by: Jonathan Gallacher
Date Added: 29 May 2019 12:57
Last Modified: 29 May 2019 13:06
URI: https://irep.ntu.ac.uk/id/eprint/36674

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