Can we detect the emotions (or feelings) of a human through conversations with an AI?
Something like a "confessional", disregarding human possibilities to lie.
Below, I have the categories joyful, sadness, anger, fear and affection. For each category, there are several words that can be in the texts that refer to it.
Joy: ( cheerful, happy, confident, happy, satisfied, excited, interested, dazzled, optimistic, relieved, euphoric, drunk, witty, good )
Sadness: ( sad, desperate, displeased, depressed, bored, lonely, hurt, desolate, meditative, defrauded, withdrawn, pitying, concentrated, depressed, melancholic, nostalgic )
Anger: ( aggressive, critical, angry, hysterical, envious, grumpy, disappointed, shocked, exasperated, frustrated, arrogant, jealous, agonized, hostile, vengeful )
Fear: ( shy, frightened, fearful, horrified, suspicious, disbelieving, embarrassed, embarrassed, shaken, surprised, guilty, anxious, cautious, indecisive, embarrassed, modest )
Affection: ( loving, passionate, supportive, malicious, dazzled, glazed, homesick, embarrassed, indifferent, curious, tender, moved, hopeful )
Phrase 1: "I'm very happy! It concludes college."
Categorization 1: - Joy (+1)
- Sadness (-1)
Phrase 2: "I'm sad, my mother passed away."
Categorization 2: - Sadness (+1)
- Joy (-1)
Phrase 3: "I met a girl, but I was ashamed."
Categorization 3: - Fear (+1)
Is this a clever way to follow and / or improve, or am I completely out of the way?
I see that there is a Google product that creates parsing according to the phrases. I do not know how it works, because I like to recreate the way I think it would work.
Remembering that this would not be the only way to categorize the phrase. This would be the first phase of the analysis. I can also identify the subject of the sentence, so we would know if the sadness is from the creator of the message or from a third party, in most cases.