Artificial Intelligence
Better understand the work morale of your colleagues, team and company as a whole.
Emotions Analysis
If both the administrator and you enable this feature, the artificial intelligence service will determine the emotions and feelings that you express in your language. You could use this service to get feedback about your communications, which could improve the effectiveness of the messages and how they are received. Also, you can provide appropriate support if you found your team is in negative emotions. English is currently supported. Additional languages will be supported in late 2017.
Emotions
Joy
Joy or happiness has shades of enjoyment, satisfaction and pleasure. There is a sense of well-being, inner peace, love, safety and contentment.
Fear
A response to impending danger. It is a survival mechanism that is a reaction to some negative stimulus. It may be a mild caution or an extreme phobia.
Sadness
Indicates a feeling of loss and disadvantage. When a person can be observed to be quiet, less energetic and withdrawn, it may be inferred that sadness exists.
Disgust
An emotional response of revulsion to something considered offensive or unpleasant. It is a sensation that refers to something revolting.
Anger
Evoked due to injustice, conflict, humiliation, negligence or betrayal. If anger is active, the individual attacks the target, verbally or physically. If anger is passive, the person silently sulks and feels tension and hostility.
How it works
The Emotional Analysis service is based on the theory of psycholinguistics, a field of research that explores the relationship between linguistic behaviors and psychological theories.Psycholinguistics researchers have studied to understand whether the words that we use in our day-to-day lives are reflective of who we are, how we feel, and how we think. After several decades of research in this area, it is now accepted in psychology, marketing, and other fields that language reflects more than just what we want to say. The frequency with which we use certain types of words can provide clues to personality, thinking style, social connections, and emotional states. The work on Emotional Analysis builds on these foundations to infer one's tone from one's text.Emotional state is derived from the work on Emotion Analysis, which is an ensemble framework to infer emotions from a given text.
To derive emotion scores from text, a stacked generalization-based ensemble framework is used. Stacked generalization is a general method of using a high-level model to combine lower-level models to achieve greater predictive accuracy. Features such as n-grams (unigrams, bigrams and trigrams), punctuation, emoticons, curse words, greeting words (such as hello, hi, and thanks), and sentiment polarity are fed into state-of-the machine learning algorithms to classify emotion categories.Psycholinguistics researchers have studied to understand whether the words that we use in our day-to-day lives are reflective of who we are, how we feel, and how we think. After several decades of research in this area, it is now accepted in psychology, marketing, and other fields that language reflects more than just what we want to say.
The frequency with which we use certain types of words can provide clues to personality, thinking style, social connections, and emotional states. The work on Emotional Analysis builds on these foundations to infer one's tone from one's text.Emotional state is derived from the work on Emotion Analysis, which is an ensemble framework to infer emotions from a given text. To derive emotion scores from text, a stacked generalization-based ensemble framework is used. Stacked generalization is a general method of using a high-level model to combine lower-level models to achieve greater predictive accuracy. Features such as n-grams (unigrams, bigrams and trigrams), punctuation, emoticons, curse words, greeting words (such as hello, hi, and thanks), and sentiment polarity are fed into state-of-the machine learning algorithms to classify emotion categories.