Especially texts and comments of e.g. employee surveys provide valuable clues to opinions and ideas. Whereas these qualitative data were previously not analyzed at all or just very costly, our automatic analysis offer unforeseen opportunities. We are able to categorize, simplify, and make huge amounts of text data usable in a quick, consistent, and objective manner.
Benefit from our machine learning algorithms to gain deeper insights from texts and clearly visualize them. The specialty: In contrast to conventional textanalyses, which often define categories and topics beforehand, our methods exclusively use the data that you supplied – without being put into any pre-defined “pigeon-holes”.
At the same time, complex interactions between words, sentences, and complete comments are included that widely exceed keywords. Hence, the truly relevant topics are identified with the highest methodological quality. For precise and individual results.
In Topic Modeling unsupervised learning algorithms distribute texts into different topics. The individual topics are separated as clearly as possible from each other and intrinsically closed in themselves. These are calculated on the basis of word distributions, interactions of words in single texts and over all texts. The optimal amount of topics is determined by the coherence criterion.
The Sentiment Analysis detects simple moods and attitudes in texts. These are usually grouped into positive, negative, and neutral. The algorithm uses supervised learning that learned on previously categorized data.
The Part-of-Speech Tagger assigns words and punctuation marks of texts to specific parts of speech in order to better understand the structure of sentences. This algorithm uses information on context as well as definitions of words.
Whether general requests or questions to specific topics – Approach us!
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