With the rise of social media, sentiment analysis has become a popular research field in recent years. Sentiment dictionaries are vital for this task; however, there are few available in Chinese. Translated English sentiment dictionaries are often inaccurate or lacking vocabulary. Moreover, many dictionaries provide only binary polarity values or no values at all. In this paper, we integrate several common sentiment dictionaries into a larger dictionary. We then describe an algorithm that can extrapolate sentiment values for the empty entries in this dictionary from the annotated ones using the relationships in Chinese ConceptNet. The resulting iSentiDictionary is a Chinese sentiment dictionary with 28,248 concepts annotated with sentiment values. To test our dictionary, we construct a dictionary-based song lyrics sentiment analysis system. We compare performance of iSentiDictionary with that of three translated sentiment dictionaries: ANEW, SenticNet and SentiWordNet. Experimental results show that using iSentiDictionary achieves the best results in terms of both Word Coverage and Absolute Sentiment Error. We believe this is because of iSentiDictionary’s high word coverage for Mandarin pop songs.