With the General Election quickly approaching, Text Mining Solutions continues to search for answers in an election season full of questions, posed by both the public and the parties. The algorithm has now processed over 33 million tweets which reveal views of the voters across a breadth of areas. This week TMS has been responding to numerous ‘experts’ on Twitter, using various topic heat maps which the algorithm has generated to endorse various points being made, or indeed oppose them.

The algorithm collects tweets and subsequently text mines them in order to discover who is talking, and what they’re talking about. This allows tweets to be broken down into categories, where TMS is then able to extract huge levels of information and place it into the database. At this point, the data is then refined further by establishing content details, including the sentiment and even the location of the tweeter.

Below are two maps which display TMS algorithm results from Wednesday 27th November. Both display the topics in conversation and connections between them for each party across Twitter.

 

 

With such information, Text Mining Solutions is able to work within the political sphere, using data to reiterate or disprove comments, predictions and debates in the run up to the election. This has been true in two cases this week, which those of you who follow our Twitter may already be aware of.

Firstly, TMS responded to a tweet by Jennie Formby, the General Secretary of the Labour Party, where she shared her exclusive article for the Jewish News. The article was her response to Chief Rabbi Mirvis’ criticism of Corbyn and Labour, by highlighting actions being taken to tackle antisemitism in the Party. Whilst this may be the case, TMS is able to demonstrate that antisemitism in the Labour Party is still an issue being heavily discussed by Twitter users. The topic heat map below reveals the volume of tweets mentioning the Labour Party and various topics of interest in the run up to the election, and antisemitism is clearly the most common mention, alongside phrases such as antisemitism record, chief rabbi attack, and labour antisemitism crisis. With this information, the party can see it is still a clear issue, which they should therefore continue to address.

A further example is Text Mining Solutions’ ability to endorse news journalist and Preston co-presenter, Anushka Athana’s tweet, regarding the Tory’s failure to address islamophobia. The topic heat map below, similar to the Labour one, reveals topics being discussed across Twitter which mention the Conservative Party. Aside from the NHS and the Tory manifesto, frequent mention of racism shows that Islamophobia is still an issue for the Conservative Party.

The algorithm is a reliable alternative to the polls, and is able to provide more frequent, varied and up-to-date information. As demonstrated in this week’s blog post, Text Mining Solutions can process enormous quantities of data, with numerous refined searches which allows for a detailed and specific analysis of text.