  The rise in popularity and ubiquity of Twitter has made sentiment analysis of
tweets an important and well-covered area of research. However, the 140
character limit imposed on tweets makes it hard to use standard linguistic
methods for sentiment classification. On the other hand, what tweets lack in
structure they make up with sheer volume and rich metadata. This metadata
includes geolocation, temporal and author information. We hypothesize that
sentiment is dependent on all these contextual factors. Different locations,
times and authors have different emotional valences. In this paper, we explored
this hypothesis by utilizing distant supervision to collect millions of
labelled tweets from different locations, times and authors. We used this data
to analyse the variation of tweet sentiments across different authors, times
and locations. Once we explored and understood the relationship between these
variables and sentiment, we used a Bayesian approach to combine these variables
with more standard linguistic features such as n-grams to create a Twitter
sentiment classifier. This combined classifier outperforms the purely
linguistic classifier, showing that integrating the rich contextual information
available on Twitter into sentiment classification is a promising direction of
research.
