We look at the era of Thatcher’s Government in order to identify alliances and enmities between politicians in the British parliament. As a resource we take Hansard, the UK parliamentary debate record (http://hansard.millbanksystems.com) as a genre that has a relatively high density of appeals to ethos. We analyse these data using OVA+: Online Visualisation of Arguments software. We construct our publicly available corpus by extracting 90 transcripts from Margaret Thatcher’s period as Prime Minister, dating from 1979 to 1990 (a volatile period in the UK). We split our data into 60 training transcripts, of which we use 10% as validation data, and 30 test transcripts to give a wide range of test cases. Each transcript, part of a day parliament sitting, contains an initial question asked by a member of parliament (MP) identified by their name and constituency, to which a reply is given by a government minister and further continued with subsequent turns in the debate.

We aim to extract the relationships between politicians or between a politician and a party  through ethotic sentiment expressions, ESEs: when a politician talks about another politician’s or party’s ethos; and then to classify these relationships as having positive or negative sentiment through +/-ESEs: when a politician supports, +ESE, or attacks, -ESE, ethos of another politician or party. The intuition that we intend to model is that linguistic structure  encodes  both the target entity of the ethotic statement (in Example 1 below, “My hon. Friend,” and in Example 2, “the Government”) as well as the polarity of the ethotic statement (in Example 1, positive sentiment is signalled by “assiduously”, “pursuing” and “interests” and in Example 2, negative sentiment is signalled by  “sick”). As a result, we are able to identify alliances and enmities between politicians.

Example 1 Mr. John Moore said, My hon. Friend is assiduously pursuing his  constituents’ interests.

Example 2 Mr. Bruce Grocott said, Is it not the simple truth that the Government are making the country sick?

This work was supported by EPSRC in the UK under grant EP/M506497/1.