Authors - Pelumi O. Bamgboye, Marion O. Adebiyi, Abayomi A. Adebiyi, Francis B. Osang, Ayodele A. Adebiyi, Miracle Enwere, Abednego Shekari Abstract - Customer review is constantly generated in the form of text based on customer opinion concerning specific products in the electronic (e)-commerce space. With the proliferation of data, the classification of customers’ opinions is a major issue in e-commerce. However, merchants frequently require customers to input their opinion about a product, in order to harvest customer experiences. The text classification technique helps customers and merchants to know if a product has a positive or negative review. This will enable merchants to improve the customer experience and also improve the revenue of the company. Sentiment analysis was executed on a customer product review dataset using support vector machine (SVM) in this study. The experimental results obtained show an accuracy of 86.67% in classifying customers’ opinions on selected products. The findings will guide merchants to know customer feedback and identify their needs to improve sales culminating in increased revenue.
