[Objective] This paper proposes a fine-grained sentiment analysis method based on Convolutional Neural Network(CNN). [Methods] First, we incorporated attribute features into the word vector model. Then, we extracted the keyword sets of the comments statistically based on the fine-grained attributes of products or services. Third, we constructed the eigenvectors of the comments with attributes of the target objects. Finally, we trained the modified CNN model to add the affective clustering layer of the input text vector. [Results] Compared with the traditional emotion classification model, the training results of the new CNN model were significantly improved in terms of precision, recall and F-score. [Limitations] Only examined the new model with comments from one field. [Conclusions] The fine-grained sentiment analysis method based on convolutional neural network can dramatically improve the precision of sentiment classification.
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