Pick a random tweet from a public hashtag. Put aside all knowledge you have of major sporting events, news & current affairs. Pick how 'happy' it is from 1-9.
It's also worth pointing out that the data-set used in this study to gauge 'sentiment' is based on firm psychology and actually infers much more than just perceived happiness.
Effectively, yes. It's called the Wisdom of the Crowd: http://en.wikipedia.org/wiki/Wisdom_of_the_crowd
To rule out artefacts you need to work out a) what you're looking for, and b) whether it is backed up by anything else. For example, in this study my findings are backed up by other, different studies. The findings also correlate with key public events.
Being able to infer true sentiment is not what is claimed here. Instead, you're able to infer 'sentiment' trends that are backed up in some way by other studies and research.
I'm 100% sure that these approaches aren't perfect, however they are proving useful. For example, one group of people are using a very similar approach to take average 'sentiment' on Twitter and use it to predict stock market fluctuations 3 days in advance. It works and it's proven not to be fluke. Something is in the results, however inaccurate a single tweet is.
I'm well aware of the concept of the wisdom of the crowd but if your incoming data is noise then your ability to build any kind of aggregate analysis on top of it is going to be nil & Post-hoc analysis means you can find only things you already know are there. ANEW/AFINN etcetc are basically the white flag to any kind of meaningful automated analysis of tweets and resorting to simply dumb word counts instead. Yes, you can capture a broad pattern but the shape and strength of that? The contours are an artifact of the list you use, it's utterly arbitrary. Throw a few more random phrases on the list, assign them some arbitrary values and presto, new results! If an tweet goes round twitter in a minute with hundreds of thousands of RTs this kind of analysis will miss it completely unless it's lucky enough to be preloaded with the right dictionary. To work in this kind of context an algorithm has to be able to trim its own sails.
There's nothing particularly wrong with your work, I'm just sick of the cycles wasted attempting to do an extreme version of the problems that NLP already struggles with.