Sentiment analysis – mining text to see what people are talking about and how they feel about it – is based on algorithms and software libraries that were created and configured by people who’ve made a series of small, accumulative decisions that affect what we see. You can think of sentiment analysis as a sausage factory with the text of tweets as the mince going in one end, and pretty pictures as the product coming out the other end. A healthy democracy needs the list of secret ingredients added during processing, not least because this election prominently features spin rooms and party lines.
The software used for sentiment analysis is ‘trained’ on existing text, and the type of text used affects what the software assumes about the world. For example, software trained on business articles is great at recognising company names but does not do so well on content taken from museum catalogues (unless the inventor of an object went on to found a company and so entered the trained vocabulary). The algorithms used to process text change the output, as does the length of the phrase analysed. The results are riddled with assumptions about tone, intent, the demographics of the poster and more.