... the foundation of our work involves combining multiple large collections of texts of natural language going back two centuries with state-of-the-art methods for deriving public mood (i.e. sentiment) from language. The recent digitisation of books, newspapers and other sources of natural language – such as the Google Books Ngram database – represent historically unprecedented amounts of data (‘big data’) on what people thought and wrote over the past few centuries (see Michel 2011). These databases have already proved fruitful in detecting large-scale changes in language, which in turn correlate with social and demographic change, for instance in Hills and Adelman (2015).Now all they need is some proof that the sentiments found in literature, newspapers and pamphlets are an accurate representation of the public's moods. According to their graph the happiest days in the USA were the early 1920s and pretty much continuously declined until the 1990s. And life was a cabaret in both Weimar Germany and the Nazi years (if we are reading them correctly).
These data offer the capacity to infer public mood using sentiment analysis. Deriving sentiment from large collections of written text represents a growing scientific endeavour.
Also, Maggie Thatcher did Britain of world of good (happiness-wise), it seems.
Well, soldier on;
To overcome this [inability to use the data to predict], especially at the government level, we must develop our capacity to predict how wellbeing responds to both deliberate and unexpected events. Better predicting economic fortunes was the motivation of the national income accounting, which later became GDP, following the Depression in the 1930s. Of course, now numerous decisions are based on GDP, despite a near global acceptance that, in the words of John F Kennedy, “it measures everything in short, except that which makes life worthwhile” (Presidential Library and Museum, North Dakota).And we all know what was worthwhile to JFK; Marilyn Monroe.