  With the rise in popularity of public social media and micro-blogging
services, most notably Twitter, the people have found a venue to hear and be
heard by their peers without an intermediary. As a consequence, and aided by
the public nature of Twitter, political scientists now potentially have the
means to analyse and understand the narratives that organically form, spread
and decline among the public in a political campaign. However, the volume and
diversity of the conversation on Twitter, combined with its noisy and
idiosyncratic nature, make this a hard task. Thus, advanced data mining and
language processing techniques are required to process and analyse the data. In
this paper, we present and evaluate a technical framework, based on recent
advances in deep neural networks, for identifying and analysing
election-related conversation on Twitter on a continuous, longitudinal basis.
Our models can detect election-related tweets with an F-score of 0.92 and can
categorize these tweets into 22 topics with an F-score of 0.90.
