The prevalence of creativity in the emergent online media language calls for more effective computational approach to semantic change. Two divergent metaphysical understandings are found with the task: juxtaposition-view of change and succession-view of change. This paper argues that the succession-view better reflects the essence of semantic change and proposes a successive framework for automatic semantic change detection. The framework analyzes the semantic change at both the word level and the individual-sense level inside a word by transforming the task into change pattern detection over time series data. At the word level, the framework models the word’s semantic change with S-shaped model and successfully correlates change patterns with classical semantic change categories such as broadening, narrowing, new word coining, metaphorical change, and metonymic change. At the sense level, the framework measures the conventionality of individual senses and distinguishes categories of temporary word usage, basic sense, novel sense and disappearing sense, again with S-shaped model. Experiments at both levels yield increased precision rate as compared with the baseline, supporting the succession-view of semantic change.