Is it possible to guess a user's mood based on the structure of text?

Question

I assume a natural language processor would need to be used to parse the text itself, but what suggestions do you have for an algorithm to detect a user\'s mood based on tex

Answer 1

Yes.

Whether or not you can do it is another story. The problem seems at first to be AI complete.

Now then, if you had keystroke timings you should be able to figure it out.

Answer 2

I can't believe I'm taking this seriously... assuming a one-dimensional mood space:

• If the text contains a curse word, -10 mood.
• I think exclamations would tend to be negative, so -2 mood.
• When I get frustrated, I type in Very. Short. Sentences. -5 mood.

The more I think about this, the more it's clear that a lot of these signifiers indicate extreme mood in general, but it's not always clear what kind of mood.

Answer 3

My memory isn't good on this subject, but I believe I saw some research about the grammar structure of the text and the overall tone. That could be also as simple as shorter words and emotion expression words (well, expletives are pretty obvious).

Edit: I noted that the first person to answer had substantially similar post. There could be indeed some serious idea about shorter sentences.

Answer 4

I think, my algorythm is rather straightforward, yet, why not calculating smilics through the text :) vs :(

Obviously, the text ":) :) :) :)" resolves to a happy user, while ":( :( :(" will surely resolve to a sad one. Enjoy!

Answer 5

Fuzzy logic will do I guess. Any way it will be quite easy to start with several rules of determining the user's mood and then extend and combine the "engine" with more accurate and sophisticated ones.

Answer 6

If you support fonts, bold red text is probably an angry user. Green regular sized texts with butterfly clip art a happy one.