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A novel method of constructing sentiment lexicon of new words (SLNW) is proposed to realize effective Weibo sentiment analysis by integrating existing lexicons of sentiments, lexicons of degree, negation and network. Based on left-right entropy and mutual information (MI) neologism discovery algorithms, this new algorithm divides N-gram to obtain strings dynamically instead of relying on fixed sliding window when using Trie as data structure. The sentiment-oriented point mutual information (SO-PMI) algorithm with Laplacian smoothing is used to distinguish sentiment tendency of new words found in the data set to form SLNW by putting new words to basic sentiment lexicon. Experiments show that the sentiment analysis based on SLNW performs better than others. Precision, recall and F-measure are improved in both topic and non-topic Weibo data sets. [ABSTRACT FROM AUTHOR]