Review spam (fake review) detection is increasingly important taking into consideration the rapid growth of internet purchases. Therefore, sophisticated spam filters must be designed to tackle the problem. Traditional machine learning algorithms use review content and other features to detect review spam. However, as demonstrated in related studies, the linguistic context of words may be of particular importance for text categorization. In order to enhance the performance of review spam detection, we propose a novel content-based approach that considers both bag-of-words and word context. More precisely, our approach utilizes n-grams and the skip-gram word embedding method to build a vector model. As a result, high-dimensional feature representation is generated. To handle the representation and classify the review spam accurately, a deep feed-forward neural network is used in the second step. To verify our approach, we use two hotel review datasets, including positive and negative reviews. We show that the proposed detection system outperforms other popular algorithms for review spam detection in terms of accuracy and area under ROC. Importantly, the system provides balanced performance on both classes, legitimate and spam, irrespective of review polarity.