Marius Sältzer

Temporality is an important aspect of political discourse. Politicians and policymakers attempt to construct the past and the future to gain power, legitimize their policies, claim success for themselves and blame others. To make computational analysis of temporality more accessible, we develop a new methodology using a semisupervised machine-learning algorithm called Latent Semantic Scaling. Only with a set of common verbs in the past perfect and future tense as seed words, the algorithm estimates the temporality of all other words. We demonstrate that it can identify temporal orientation of English and German sentences from election manifestos around 60–70% accurately, which is comparable to the results from a recent study based on supervised machine-learning algorithms. We also apply it to Twitter posts by German political parties to reveal temporal orientation of policy issues.