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Sentiment Analysis of Customer Reviews: Balanced versus Unbalanced Datasets

Burns, Nicola, Bi, Yaxin, Wang, Hui and Anderson, Terry (2011) Sentiment Analysis of Customer Reviews: Balanced versus Unbalanced Datasets. In: Knowledge-Based and Intelligent Information and Engineering Systems. Springer Berlin Heidelberg, pp. 161-170. ISBN 978-3-642-23850-5 [Book section]

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More people are buying products online and expressing their opinions on these products through online reviews. Sentiment analysis can be used to extract valuable information from reviews, and the results can benefit both consumers and manufacturers. This research shows a study which compares two well known machine learning algorithms namely, dynamic language model and naïve Bayes classifier. Experiments have been carried out to determine the consistency of results when the datasets are of different sizes and also the effect of a balanced or unbalanced dataset. The experimental results indicate that both the algorithms over a realistic unbalanced dataset can achieve better results than the balanced datasets commonly used in research

Item Type:Book section
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:25478
Deposited By: Dr Yaxin Bi
Deposited On:20 Jan 2016 12:31
Last Modified:20 Jan 2016 12:31

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