Sentiment analysis has demonstrated that the automation and computational recognition of sentiments is possible and evolving, due to factors such as; emergence of new technological trends and the continued dynamic state of the human language. Sentiment analysis is therefore an Information extraction task that aims at obtaining private sentiments that can either be expressed as positive or negative, toward a specific object or subject. However, social media platforms are marred with informal texts that make extraction and parsing of relevant information a problem for most systems and models. This can pose a challenge to companies, individuals or organizations that need to make specific business decisions based on the available data. To overcome such inefficiencies, this research proposes an ensemble model on the basis of performance evaluation on sentiment classification of product reviews. The research will explore the use of a detailed pre-processing technique with the integration two classifiers, Naïve Bayes and SVM as an ensemble. The effect (in terms of performance measure and evaluation) of such a computational model, and how the model can be implemented within machine learning approaches to sentiment analysis, has formed grounds for this research.
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RUFORUM Working document series
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