Can we detect the emotions (or feelings) of a human through conversations with an AI? Can we detect the emotions (or feelings) of a human through conversations with an AI?
Something like a &quot;confessional&quot;, 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Sadness: ( sad, desperate, displeased, depressed, bored, lonely, hurt, desolate, meditative, defrauded, withdrawn, pitying, concentrated, depressed, melancholic, nostalgic )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 )


Flow Example
Phrase 1: &quot;I'm very happy! It concludes college.&quot;
Categorization 1:
 - Joy (+1)

Sadness (-1)


Phrase 2: &quot;I'm sad, my mother passed away.&quot;
Categorization 2:
 - Sadness (+1)

Joy (-1)


Phrase 3: &quot;I met a girl, but I was ashamed.&quot;
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.

NLTK
Sentiment Analysis Python Example
I think you are definitely on a very sensible track. No one defines right or wrong in emotion field. It's not hard science. It's all theories.  

I have recently read a paper regarding emotions in Reinforcement Learning (RL). It has explained briefly emotion from 3 perspectives: psychology, neuroscience and computer science. In particular, your way of emotion definition matches one of the categorical emotion theory in the psychology perspective. The other theories in psychology perspective and componential emotion. You can try to implement them and try out which one works well. The paper has also introduced ways to measure the level of emotions (emotion elicitation).

Here is the link for the paper I have mentioned. I am sure you will receive lots of inspiration. I have also written a summary of this paper. Take a look if the original paper is too long to read.

I don't have any concrete solution for implementing. But the general idea is always trying to categorize abstract concepts and quantify them. And try some thing, and iteratively modify and improve it. All the best!