How to identify the semantic orientation of terms and build a high-quality sentiment dictionary to improve the accuracy of sentiment analysis on Micro-blogs has significant importance. Traditional algorithms based on corpus are sensitive to the seed words, and cannot effectively identify semantic orientation identification on low-frequency terms. To solve this problem, an algorithm based on word affinity measure is proposed to identify the semantic orientation of terms from Chinese Micro-blogs. Firstly, candidate words are extracted by the part of speech combination patterns. Secondly, Micro-blog emoticons are selected as seed words, and word affinity networks are built. Then, low frequency words are expanded by a synonyms dictionary during calculating the semantic orientation similarity between candidate words and seed words. Finally, the semantic orientation is determined according to the threshold. Experiments are conducted on a corpus with two million Micro-blogs using the proposed algorithm and traditional algorithms respectively. Experimental results show the advantage of the proposed algorithm.