Psychology with AI makes the use of language more successful
Language expresses the personality of every human being.
Our aim is to better interpret their language in order to understand their personality better. This is why we have developed an artificial intelligence that also reads between the lines.
Our unique Psychological AI identifies mood, motives and personality traits and delivers highly reliable results.
The results are based on a comprehensible procedure, which we take for granted when analysing the personality of people. For us, it is above all the prerequisite for trustful cooperation, because we are aware of our responsibility and take it extremely seriously.
- IDENTIFY IMPLICIT MOTIVES
- VALIDATED WITH INDEPENDENT DATA
- ACQUISITON FROM REAL SPEECH
- NO BLACKBOX
- LEADER IN THE RECOGNITION OF IMPLICIT MOTIVES FROM LANGUAGE
CONSCIOUSLY NOTICING THE SUBCONSCIOUS
WE MEASURE DIFFERENT PSYCHOLOGICAL TRAITS
The frequency with which we use certain words and word combinations reveals something about personality.
This connection, which many would intuitively assume, has already been shown and replicated in many scientific studies. In the development of our 100 word analysis numerous psychological studies were considered, in which experienced researchers from different disciplines were involved. Especially the American social psychologist, J. Pennebaker, contributes a lot to the understanding of the connection between language and personality.
Through his work, we know that it is the seemingly unimportant function words (e.g. personal pronouns or articles) that reveal the most about a person. Functional words are sentence-logical elements that do not transport content, but structure content words and thus give them a linguistic framework. In the following we will give some examples of how the linguistic structure reveals something about the personality of people.
How people think - rather analytically or intuitively - is reflected in their use of function words. For example, people with an analytical way of thinking increasingly use function words that give their language accuracy, such as articles or prepositions. Researchers discovered this connection when they examined letters of motivation from students. The dominance of people can also be derived from language. Several studies investigated how dominant people discuss language in comparison to other members of a group. Finally, there are also some findings that point to a connection between depression and language. Repeated studies have shown that the personal pronoun "I" is used more often by depressed people than by people without depression.
The use of words reveals something about a person's current emotional state: Is she sad, angry or happy at the moment? First, emotions can be divided into positive and negative valences. In addition, negative emotions in particular can be further subdivided, with the most relevant negative emotions being grief, anger and fear. Moods and feelings, on the other hand, are expressed by so-called content words.
People are guided by motives.
Knowing what drives us is especially important in the professional context, because it provides information about which career path is the right one. But what are motives anyway? Motives are internal guides to action. They make us think in a certain direction and behave in a certain way. They express a striving: "I would like to be ..." or "I would like to be ...".
The motives based on the theory of McClelland
In general, three basic motives are distinguished. These are the motives of power, status and leadership, performance and development and harmonious relationships with other people. These motives were formulated by the social psychologist David McClelland and have been largely researched in psychology. However, these motives are not evenly distributed, but are somewhat different for each of us: one person is important to maintain good relationships with others, while another strives for admiration. These motives arise from our upbringing, our social environment and our experiences. Often we cannot even say which motive actually drives us. Such unconscious motives are called implicit. Implicit motives have so far been measured with so-called projective methods. People are presented with pictures with ambiguous contents, for which they are supposed to invent a story. The implicit motives of the narrators are reflected in their stories. In other words, people project their unconscious motives onto a neutral situation. As has been shown in long-term studies, the process also provides information about professional success characteristics, such as salary. However, this procedure is extremely time-consuming because the content must be evaluated by experts.
Our Psychological AI makes this evaluation superfluous through automatic analysis. And we do not only claim this, but can also prove measures of quality such as prognostic validity in our own and other studies.
AUTOMATIC CAPTURING AND PROCESSING
The 100 Worte PSYCHOLOGICAL AI captures implicit motives similar to methods that have been used successfully for a long time (e.g. Thematic Perception Test TAT). And this is how the analysis works:
- The text to be analysed is compared with our nine motif dictionaries. Individual words signal certain motives, e.g. "existed" the performance motive.
- However, before the word in question is actually counted, the context must be checked. Because this gives information about the exact meaning of the word. For example, "passed" in connection with "check" means something quite different from "bearing". We only count words if they have the right meaning for
- Finally, it is also examined whether the word in question is denied. Because even then it has a different meaning and must be evaluated differently. For this we look at whether the word in question is in a negation (e.g. "not").
Thanks to this unique technology, we are leaders in the recognition of implicit motives from language.
HOW WE USE MACHINE LEARNING
You probably give these words a different meaning because you understand the context. However, a machine always interprets these words as the word "lead. We have an understanding of the meaning of words and apply it constantly without thinking about it.
A machine must learn this understanding. In order to convey this understanding to the machine, we use different machine methods of word processing and text comprehension. These methods are referred to as Natural Language Processing (NLP).
Part-of-speech tagging (POS) is the assignment of words and punctuation marks of a text to word types such as adjectives or verbs. We use this technology to distinguish the following sentences, for example:
He attributes his success to lots of hard work. ("attributes" = VERB)
Our research has identified certain attributesof successful open source projects. ("attributes" = Plural NOUN)
Embedded Language Models (ELMo) give words a vector that also takes context into account. We use ELMo if words with different meanings cannot be distinguished by their word type.
The right brain perspective dominates his consciousness.
Both parties can benfit from the right working relationship.
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