Geoff Moes wrote a great, comprehensive writeup of our most recent Data Science DC event, "Implicit Sentiment Mining in Twitter Streams" by Maksim (Max) Tsvetovat. Geoff is a seasoned software developer and math enthusiast, with an interest in applying analytics to software engineering problems. He blogs about his thoughts at Elegant Coding. Here's an excerpt (pardon the language in the example):
The field of computational linguistics has developed a number of techniques to handle some the complexity issues... by parsing text using POS (parts-of-speech) identification which helps with homonyms and some ambiguity. [Max] gives the following example:
Create rules with amplifier words and inverter words:
- This concert (np) was (v) f**ing (AMP) awesome (+1) = +2
- But the opening act (np) was (v) not (INV) great (+1) = -1
- My car (np) got (v) rear-ended (v)! F**ing (AMP) awesome (+1) = +2??
Here he introduces two concepts which modify the sentiment, which might fall under the concept of sentiment "polarity classification" or detection. One idea is of an amplifier (AMP) which makes the sentiment stronger and an inverter (INV) which creates an opposite sentiment. I found this idea of "sentiment modification" intriguing and did a little searching and came across a paper called "Multilingual Sentiment Analysis on Social Media" which describes these ideas [page 12] and a few more including an attenuator which is the opposite of an amplifier. It also describes some other modifiers that control sentiment flow in the text, pretty interesting concepts, actually the paper looks quite interesting,