Sentiment analysis for images on microblogging by integrating textual information with multiple kernel learning

Image is one of the most important means to express users' emotions on microblogging, like Sina Weibo. More and more people post only images on it, due to the fast and convenient nature of image. Taking a post only using images on microblogging has been a new tendency. Most existing studies about sentiment analysis on microblogging focus on the text, or integrate image as an auxiliary information into text, so they are not applicable in this scenario. Although a few methods related to sentiment analysis for image have been proposed, most of them either ignore the semantic gap between low-level visual features and higher-level image sentiments, or require a lot of textual information in the phases of both training and inference. This paper proposes a new sentiment analysis method based on Simple Multiple Kernel Learning (SimpleMKL). Specifically, textual information as a sort of sufficiently emotional source data, we can use it to promote the ability via SimpleMKL to classify images. And once we get the image classifier, none of texts are needed when predicting other unlabelled images. Experimental results show that our proposed method can improve the performance significantly on data we crawled and labelled from Sina Weibo. We find that our method not only outperforms some common methods, like SVM, Naive Bayes, KNN, Random Forest, Adaboost, etc., using the image features of colour, hog, texture, but also outperforms some state-of-the-art methods.