In order to effectively improve the accuracy of sentiment orientation and intensity analysis of unstructured Web financial text, a sentiment analysis algorithm for Web financial text based on semantic rule (SAFT-SR) was proposed. The algorithm extracted features of financial text based on Apriori, constructed financial sentiment lexicon and semantic rules to recognize sentiment unit and intensity, and figured out the sentiment orientation and intensity of text. Experiment results demonstrate that SAFT-SR is a promising algorithm for sentiment analysis on financial text. Compared with Ku’s algorithm, in sentiment orientation classification, SAFT-SR has better classification performance and increases F-measure, recall and precision; in sentiment intensity analysis, SAFT-SR reduces error and is closer to expert mark.