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Although the sentiment analysis of tweet has caused more and more attention in recent years, most existing methods mainly analyze the text information. Because of the fuzziness of emotion expression, users are more likely to use mixed ways, such as words and image, to express their feelings. This paper proposes a classification method of tweet emotion based on fusion feature, which combines the textual feature and the image feature effectively. Due to the sparse data and the high degree of the redundancy of the classification feature, we adopt the canonical correlation analysis to reduce dimensions of data expressed by the text emotional feature and image feature. The dimension reduction of data can maximally retains the relevance of characteristics of the text and the emotional image on the high-level semantic and utilize the support vector machine (SVM) to train and test the feature fusion data set. The results of data experiment on Sina tweet show that the algorithm can obtain better classification effect than the single feature selection methods.

Micro-Blog Emotion Classification Method Research Based on Cross-Media Features