Natural Language Understanding becomes very hard when creativity and figurative language are used in social communication. Humor constitutes an illustrative example of how humans use creative language to produce funny content. Therefore, create new methods and resources for analyzing properly humorous texts is an important issue in Natural Language Processing (NLP) and even more in Human Computer Interaction (HCI). In this sense, this paper introduces our UO UPV system developed for the Humor Analysis based on Human Annotation (HAHA) track proposed in IberEval 2018 Workshop. The task focuses on classifying tweets in Spanish as humorous or not, and predicting how funny they are. To solve this task, our proposal combines both linguistic features and an Attention-based Recurrent Neural Network, where the attention layer helps to calculate the contribution of each term towards targeted humorous classes. Experimental results show that our model achieves encourage results.