We present a quantitative definition of reputation risk, formulated in terms of a reputation time series comprising daily sentiment measurements. Self Supported Learning is used to quantify reputation risk by progressively refining an initial proposal for a Minimum Acceptable Sentiment, calculated from descriptive statistics of the reputation data. The derived values are validated using a “sense test” based on a Loess quantile. The results show that the Minimum Acceptable Sentiment value is given approximately by a two standard deviation lower tail of the observed data.