There has been significant recent interest in using the aggregate sentiment from social media sites to understand and predict real-world phenomena. However, the data from social media sites also offers a unique and—so far—unexplored opportunity to study the impact of external factors on aggregate sentiment, at the scale of a society. Using a Twitterspecific sentiment extraction methodology, we the explore patterns of sentiment present in a corpus of over 1.5 billion tweets. We focus primarily on the effect of the weather and time on aggregate sentiment, evaluating how clearly the wellknown individual patterns translate into population-wide patterns. Using machine learning techniques on the Twitter corpus correlated with the weather at the time and location of the tweets, we find that aggregate sentiment follows distinct climate, temporal, and seasonal patterns.
|Title of host publication||Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media|
|Publication status||Published - 2012|
|Event||6th International AAAI Conference on Weblogs and Social Media (ICWSM 2012) - Dublin, Ireland|
Duration: 4 Jun 2012 → …
|Conference||6th International AAAI Conference on Weblogs and Social Media (ICWSM 2012)|
|Period||04/06/2012 → …|