Analyse & Optimize Content for Emotion
Toneapi is a data analytics tool that enables a deeper emotional understanding of content to deliver better, more powerful and appropriate output. Analyse any content to understand the emotional impact, intensity and emotional themes that are being expressed in any content.
Understand content tone, intensity and emotional themes
We are pioneering a new approach in marketing, we call it Emotionally Intelligent Insights and it’s a patent pending process developed by Adoreboard’s data scientists. This state-of-the-art technology can not only understand the emotional dynamics within content but makes suggestions on how to improve the overall tone. This innovation is known as toneapi’s Emotional Thesaurus. Our expertise in this area of artificial intelligence is based on a combined experience of over three decades.
Toneapi in 60 Seconds
Brands, marketers and writers use toneapi to improve content based on emotional insights and ensure content packs an emotional punch. Data analysts use toneapi as to analyse content at scale and to visualise the results.
"Using toneapi allowed us to optimise content during a campaign, resulting in better alignment with new prospects."
Features & Insights
Discover the emotional intensity of content and tailor the tone to best match your target audience.
Reveal the key themes that your target audience place emotional value so these can be optimised.
Analyse your content for over 20 emotions meaning that you can score your content for emotional intent.
Receive suggestions on improving the tone of content to better align with a desired emotional response.
A simple way to compare and contrast your content in order to test before you invest in any content.
Integrate with favourite tools
Toneapi integrates with Google Spreadsheets and Microsoft Excel to fit perfectly in your workflow.
Optimise any content for emotions
You can optimize your text to improve an overall ‘content score.’ Here’s a an example that shows how Volkswagen could have improved its initial statement following last year’s emissions scandal.
The content analysis of comments on social media aboutVolkswagen indicated that customer reaction to the crisis on an emotional level was that trust had been broken.
Analysis of Dr Winterkorn’s (former Volkswagen CEO) first statement when the crisis broke contained no emotions whereby he was effectively asked for more time to stay in the job as he announced an investigation into how millions of buyers were cheated. But it was only when the full scale of global reaction began to impact that he decided to quit and by that stage the company’s reputation – and his standing – suffered unimaginable damage.
Winterkorn’s resignation statement contained 47 percent more emotional language than the initial one, including words like ‘stunned,’ ‘misconduct,’ ‘Irregularities,’ ‘terminating,’ ‘clarification,’ ‘transparency,’ ‘trust,’ ‘grave,’ and ‘crisis.’
In this use case we can see that the content analysis could have helped Volkswagen understand the specific emotions expressed towards the brand which could have produced a different response.