Hybrid Machine Learning Techniques for Comparative Opinion Mining

Bernard Omoi Ondara, Stephen Waithaka, John Kandiri, Lawrence Muchemi


Comparative opinion mining has lately gained traction among individuals and businesses due to its growing range of applications in brand reputation monitoring and consumer decision making among others. Past research in sub-field of opinion mining have mostly explored single-entity opinion mining models and the mining of comparative sentences suing single classifiers. Most of these studies relied on a limited number of comparative opinion labels and datasets while applying the techniques in limited domains. Consequently, the reported performances of the techniques might not be optimal in some cases like working with big data. In this study, however, we developed four hybrid machine learning techniques, with which we performed multi-class based comparative opinion mining using three datasets from different domains.  From our results, the best-performing hybrid machine learning technique for comparative opinion mining using a multi-layer perceptron as the base estimator was the Multilayer Perceptron + Random Forest (MLP + RF). This technique had an average accuracy of 93.0% and an F1-score of 93.0%. These results show that our hybrid machine learning techniques could reliably be used for comparative opinion mining to support business needs like brand reputation monitoring.


Machine Learning; Machine Learning Techniques; Comparative Opinion Mining; Hybrid Machine Learning Techniques; Deep Learning Techniques

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DOI: http://dx.doi.org/10.24014/ijaidm.v6i2.22644


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