Simplistic Sentiment Mining from Tweets

By: Jeff Clark    Date: Tue, 22 Sep 2009

This is the third part in a series analyzing aspects of a years worth of tweets containing the word 'apple'. The first part of the series discussed Apple Brand References in Tweets and showed which Apple brands were referenced the most and their distribution over time. It also included word clouds showing the terms most often associated with each of the primary brands. One of these is shown below for 'ipod'.

It's interesting and gives some indication of the other topical words related to 'ipod' and their relative frequency. One thing it doesn't do is show what people feel about ipods. Do they Love them? Hate them? Can we figure it out from all this data?

One simple method of approaching this problem is to see which emotion-laden adjectives or declarations occur together with the various brands in tweets. This is a crude form of sentiment mining that makes no attempt at detecting sarcasm or the even more common inversion due to modifiers like 'not'. The size limitations of tweets mean that they seldom express ideas in a subtle or linguistically complex fashion so it might be appropriate to use such a simplistic approach - especially when we are dealing with large volumes of tweets like we are here (570,464).

I have repeated the word association analysis done in Apple Brand References in Tweets but have restricted the words of interest to a small set of terms that are often used to express feelings. Have a look:

There appears to be considerable variation in the spectrum for the different brands. People seem to find 'iphone', 'ipod', 'nano', and 'shuffle' to be cool and interesting. They love the 'mac' and are much more negative towards 'itunes'. I suspect this technique might indeed be valuable.


Company References in Tweets
Obama UN Speech StreamGraph