Chinese multi-dimensional sentiment detection task is a big challenge with a great influence on semantic understanding. Irony is one of the sentiment analysis and the datasets established in the previous studies usually determine whether a sentence belongs to irony and its intensity. However, the lack of other sentimental features makes this kind of datasets very limited in many applications. Irony has a humorous effect in dialogues, useful sentimental features should be considered while constructing the dataset. Ironic sentences can be defined as sentences in which the true meaning is the opposite of the literal meaning. To understand the true meaning of a ironic sentence, the contextual information is needed. In summary, a dataset that includes dimensional sentiment intensities and context of ironic sentences allows researchers to better understand ironic sentences. The paper creates an extended NTU irony corpus, which includes valence, arousal and irony intensities on the sentence-level; and valence and arousal intensities on the context-level, which called the Chinese Dimensional Valence-Arousal-Irony (CDVAI) dataset. The paper analyzes the difference of CDVAI annotation results between annotators, and uses a lot of deep learning models to evaluate the prediction performances of CDVAI dataset.