Emoticons have become an important component of network language and is one of the main characteristics of the analysis of social media sentiment. The current social media sentiment analysis methods most focus on Emoji

while there is no study on the sentiment trend of kinesics. In order to obtain the multi-dimensional sentiment polarity of Chinese social media and analyze the group sentiment trend on hot topics

this paper proposes a new multi-dimensional sentiment classification method based on deep learning

which combines Emoticons with short texts. In this framework

the text and Emoji combination and the kinesics in microblog sentences were analyzed using deep learning model

and seven sentiment intensities of the two parts were obtained to explore the correlation between each part and sentiment labels. Then

a computational model was designed to reflect the multi-dimensional sentiment polarity contained in microblog sentences

which can realize the detection of the multi-dimensional sentiment intensity of sentences. The experiment utilized the NLPCC2014 dataset and the crawled microblog dataset containing kinesics for verification. Results show that when the proportion of the text and Emoji combination and the kinesics were 0.6 and 0.4

the effect of sentiment classification was the best. The sentiment classification performance indicator of the sentences containing kinesics was always higher than that without kinesics

which indicates that the combination of Emoticon and short texts can effectively improve the accuracy of microblog sentiment detection. The experiment provides a more fine-grained analysis for group sentiment trend and a new idea for Chinese social media sentiment analysis.