There are thee major challenges for an engine analyzing text for mood, tone, style and sentiment.
Context, Subjectivity and Multiple definitions.

For example, one of the biggest issues is that it has trouble understanding, humor, irony, sarcasm etc.
Even humans have trouble, as they can analyze it with 80% accuracy.
Other problems are when words have multiple definitions.
There are a few engines that use deep learning to help them understand context.

But there is a completely different approach to understand context as well as tone and mood.
We can analyze a non-verbal elements of a language (text aura, halo.. you name it).
Because it carries its own context and semantic.

The Main Idea

"Style is a very simple matter; it is all rhythm. Once you get that, you can't use the wrong words. Now this is very profound, what rhythm is, and goes far deeper than any words. A sight, an emotion, creates this wave in the mind, long before it makes words to fit it".
Virginia Woolf on Writing and Consciousness

Continuity of consciousness vs discreteness of language

Vasily Nalimov (Russian philosopher, mathematician and visionary) 1910 - 1997 -  stresses the continuous nature of consciousness, with which a person is always in contact, but which cannot be reduced to the discreteness of language (except partially through rhythmical texts).
Phrases constructed over discrete symbol-words are always interpreted at the continuous level. "The continuous nature of everyday language finds its expression in the limitless divisibility of the verbal meanings, while the continuous nature of the morphology of the animate world is expressed by the impossibility of constructing a discrete taxonomy".

"The text is organized so that the words do not limit one another but, on the contrary, have their meaning broadened, smoothly flowing into one another and merging into one stream... ...rhythm is something much more significant; rhythm probably means the dissolving of word meanings, their merging into a continuous, inwardly indissoluble stream of images. In other words, rhythm provides an opportunity for non-Bayesian reading of the texts... in a rhythmically organized text, everything happens otherwise" V. Nalimov.

Our Solution

We have made some innovations in the field of machine learning (since traditional techniques are not effective enough to meet our needs).

We apply these adapted algorithms to different levels of semantics that really exist.
Analyzing semantic of the rhythm, melody of the speech and other nonverbal language elements.
Because these magical elements provide context and real meaning.

Our goal is to bring these missing elements to metadata.

Our technology can be used:

- As a new generation book discovery systems. It can find latent connections between
different books and authors.
- As a new layer of book metadata beyond bibliographical and semantic information.
- As a marketing tool for book publishers and writers who want to address a particular audience with which they may not be familiar.

If you want to check it out, please contact us.
API will be available soon as well.