Abstract

In e-commerce, one of the most critical and important aspects of the business model is customer reviews. Customer reviews reflect the satisfaction of customers with respect to the products and services offered. E-commerce is driven by significant amounts of data which poses a huge challenge of collection and evaluation to have an insight before decision-making and business strategy implementations. The field of natural language processing and machine learning techniques have provided significant leaps in helping the analysis of big data and business analytics. Also, Recurrent Neural Networks (RNN) evolved in so many powerful algorithms and one of those is the Bi-LSTM variation of RNNs. Bi-LSTM has been identified in the literature as a suitable machine learning classification algorithm for natural language processing due to its sequential learning process. This study is an implementation of the lemmatization natural language processing technique coupled with the Bi-LSTM machine learning classification technique for customer review sentiment analysis. The application of these two techniques has reported a significant performance accuracy in sentiment analysis of customer review data. The results in this study are reported as 96.06%, 91%, and 90% for accuracy, precision, and recall respectively.

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