Fake reviews have been a major problem in online platforms with detrimental effects on customer trust. Different machine learning and natural language processing methods have been used recently to classify fake reviews from authentic ones. Due to the lack of labelled and reliable data in this domain, the right selection of input features plays a critical role in extracting the most useful and relevant information from the review content. This research investigates the impact of inconsistency between a review's rating and its sentiment score in detecting deceptive reviews. Our approach presents two sets of experiments: First with and then without the inclusion of a rating-sentiment inconsistency feature. We use deep learning classifiers and GloVe (Global Vectors for Word Representation), word embeddings to compare the performance of fake review detection models. The results indicate that incorporating the inconsistency feature in the BiGRU (Bidirectional Gated Recurrent Unit) classifier can lead the model to achieve more than 90% accuracy. However, further study is needed to demonstrate its effectiveness since inconsistency also leads to reduction in accuracy for some other models' performance.