Sentiment analysis on microblogging by integrating text and image features

Abstract

Most studies about sentiment analysis on microblogging usually focus on the features mining from the text. This paper presents a new sentiment analysis method by combing features from text with features from image. Bigram model is applied in text feature extraction while color and texture information are extracted from images. Considering the sentiment classification, we propose a new neighborhood classier based on the similarity of two instances described by the fusion of text and features. Experimental results show that our proposed method can improve the performance significantly on Sina Weibo data (we collect and label the data). We find that our method can not only increasingly improve the F values of the classification comparing with only used text or images features, but also outperforms the NaiveBayes and SVM classifiers using all features with text and images.