Reviews and comments are perceptions about specific services or products. They are embedded with hidden sentiments the reviewer has towards certain subjects. Business owners use customer reviews to understand customers’ perceptions about specific services or products. The ability to understand reviews’ sentiment from different domains or areas give decision makers and business owners the opportunity to make critical business decisions which can help them to increase profits of their businesses. Previous studies had focussed on classifying sentiment polarity by using traditional machine learning and deep learning methods. However, these suffered from low model generalization ability, causing the models to perform better only on single domain datasets rather than multiple domain datasets. The problem is the inability of the classification model to learn domain-restricted knowledge from multi-domain datasets. Aiming to improve the accuracy of the cross-domain classification, this paper proposes a method which uses Word2Seq Convolutional Neural Network (CNN) to classify reviews’ sentiment across multiple domain datasets (i.e. digital worker, movie, product, hotel and restaurant reviews). The evaluation showed that the proposed method had achieved the state-of-the-art performance. The high classification performance also promoted the reliability and effectiveness of implementing the Word2Seq CNN to classify reviews’ sentiment across different domains and learn domain restricted knowledge while improving the model generalization ability. The uploaded dataset is a sampled dataset with 5000 observations for both training and testing sets.