The UK based SME Commonplace use TransportAPI data to provide sentiment analysis using Tweets that reference any form of public transport. Every day there are thousands of tweets sent by commuters commenting on the UK transport system, but how do you make them useful as intelligence is the question? Commonplace wanted to explore this challenge by integrating both sentiment analysis and sentiment mapping.

Every day there are thousands of tweets sent by commuters commenting on the UK transport system, but how do you make them useful as intelligence?

Sentiment analysis originated in the automatic analysis of texts by computers in order to discover whether the text was broadly positive or negative. It built on a long history of trying to get machines to understand the content and tone of documents. More recently, it has been applied to social media, and it is big business. Why? Because the value of brands lies in the positive sentiment attached to them, and companies want to understand what impact their products, announcements – everything that is known publicly about the company – have on their brand reputation. This is why a company like Topsy, which specialises in sentiment analysis, was bought by Apple for more than $200m last year.

Enlightened transport operators are now taking close notice of what their customers say on social media, and in some cases also analyzing sentiment. Sentiment mapping adds location to sentiment analysis. By knowing the places that tweets or other sources of sentiment are uttered from, or the names of places they refer to, it is possible to build a map of the areas where anger is running high or where travellers are happier.

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Perhaps more importantly, operators can map the pattern of problems and fixes over time. Instead of taking a blunderbuss approach to improving their service, perhaps at huge cost in terms of engineering, they can plan more intelligently and intervene more selectively in a knowledge-led approach to the problem. During times of things like a taxi protest in London, it’s easy to see how sentiment was neutral or positive for tubes and trains, while buses, snarled up in the taxi-filled streets and saw rising levels of negative sentiment.

One of the greatest ambitions for sentiment mapping is to break down the antagonism between transport providers and the public; to redefine that relationship as aspirational and constructive, and for mutual benefit. Perhaps a few years from now, travellers will be able to make their own contribution to improving transport services, identifying waste and promoting efficient, sustainable solutions. Commonplace are enabling communities to gain better control over planning in their local neighbourhoods. In future they, and companies like them, may enable us to co-design the transport systems we need.

Transport API