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> For each timeline interaction, we counted the proportion of words expressing positive emotions (like "love", "nice", "happy", etc.) minus the proportion of wo

by evan_ 13y ago

> For each timeline interaction, we counted the proportion of words expressing positive emotions (like "love", "nice", "happy", etc.) minus the proportion of words expressing negative ones (like "hate", "hurt", "bad", etc.). I've always been suspicious of this type of measurement. It seems like statuses such as "I LOVE shoveling snow SO MUCH but I will be happy when winter is over!", where positive language is used to convey negative emotions through irony and contrast, would completely destroy such ratings. Maybe there are AIs that understand irony?

nuclear_eclipse 13y ago
I would probably naively think that sarcasm/irony goes both way, and that it's most likely to cancel out or represent a negligible portion of emotion.
bertil 13y ago
No AI understands irony perfectly, and even the most basic criticism are hard (“Thank you for the hair in my soup!“, ”Good riddance TelCo!”) However, it is irony because it's a minority use; words change their meaning if you use them too much in a different sense. This makes comparing the amount of both corpuses a crude, but relevant approach. For client work, I personally prefer to look at the length, and extend of vocabulary (Zipf rank, excluding misspellings, argumentative keywords) of the comments, because it's not really positive vs. negative as much as useless (praises or attacks) vs. arguments (usable criticism vs. structured defense) that matters on the long run. For relationship studies, I’d rather look into private messages and delay to respond (with, in Facebook case, a clear explanation that Scientist had no access to actual information, or even individual meta-data) than content.
jclos 13y ago
What you have to keep in mind in cases like this is that sentiment analysis algorithms (such as the one you mentioned) or even machine learning algorithms in general are always playing the numbers. They usually* try to have the simplest model that can achieve a sufficiently high accuracy on a large dataset.

by evan_ 13y ago

nuclear_eclipse 13y ago

I would probably naively think that sarcasm/irony goes both way, and that it's most likely to cancel out or represent a negligible portion of emotion.

bertil 13y ago

No AI understands irony perfectly, and even the most basic criticism are hard (“Thank you for the hair in my soup!“, ”Good riddance TelCo!”) However, it is irony because it's a minority use; words change their meaning if you use them too much in a different sense. This makes comparing the amount of both corpuses a crude, but relevant approach. For client work, I personally prefer to look at the length, and extend of vocabulary (Zipf rank, excluding misspellings, argumentative keywords) of the comments, because it's not really positive vs. negative as much as useless (praises or attacks) vs. arguments (usable criticism vs. structured defense) that matters on the long run. For relationship studies, I’d rather look into private messages and delay to respond (with, in Facebook case, a clear explanation that Scientist had no access to actual information, or even individual meta-data) than content.

jclos 13y ago

What you have to keep in mind in cases like this is that sentiment analysis algorithms (such as the one you mentioned) or even machine learning algorithms in general are always playing the numbers. They usually* try to have the simplest model that can achieve a sufficiently high accuracy on a large dataset.