Sentiment analysis, which refers to the task of detecting whether a textual item (e.g., a product review and a blog post) expresses a positive or negative opinion in general or about a given entity (e.g., a product, person, or policy), has received increasing attention in recent years. It serves as an important role in natural language processing. User generated content, like tourism reviews, developed dramatically during the past years, generating a large amount of unstructured data from which it is hard to obtain useful information. Due to the changes in textual order, sequence length and complicated logic, it is still a challenging task to predict the exact sentiment polarities of the user reviews, especially for fine-grained sentiment classification. In this paper, we first propose sentiment padding, a novel padding method compared with zero padding, making the input data sample of a consistent size and improving the proportion of sentiment information in each review. Inspired by the most recent studies with respect to neural networks, we propose deep learning based sentiment analysis models named lexicon integrated two-channel CNN–LSTM family models, combining CNN and LSTM/BiLSTM branches in a parallel manner. Experiments on several challenging datasets, like Stanford Sentiment Treebank, demonstrate that the proposed method outperforms many baseline methods. •We proposed sentiment padding to improve the proportion of sentiment information in each review.•We presented lexicon integrated two-channel CNN–BiLSTM model.•This paper studied the influence of the skip connection operation on two-channel deep model.•Experiment showed superiority of the proposed model on analyzing English and Chinese reviews.