Thematic was founded by Alyona Medelyan, who holds a Ph.D in Natural Language Processing and spent over 15 years researching ways to extract meaning from text. Her Ph.D at Waikato University’s renowned Machine Learning lab was sponsored by Google.
Alyona’s research covered the many ways algorithms can capture topics text, and resulted in the open-source software solution called Maui, which has been adopted by hundreds of researchers around the world. She has also published dozens of peer-reviewed articles that have been cited by more than 2600 academics.
There are a multitude of Natural Language Processing solutions, released by the best teams at Google, Amazon, Microsoft and IBM. However, they were all designed to analyze generic documents, such as news articles. Applying these NLP APIs to customer or employee feedback results in disappointing results: a long list of keywords is extracted but most of the meaning is lost.
Thematic was built for customer feedback analysis, using the latest Deep Learning research and mimics the market research guidelines on how to create code frames from customer feedback:
Thematic doesn’t presume what it needs to find in the data. It starts by identifying the most prominent themes in feedback.
Thematic uses semantic similarity to continuously create themes and construct a 2-level code frame from these themes.
While powerful, AI algorithms don't have implicit business knowledge. Thematic is built for anyone to easily teach our AI what matters to them.
Other solutions in the market typically offer three kinds of approaches. These solutions take months to setup (apart from word clouds), Thematic provides insights in just minutes.