  Twitter as a new form of social media potentially contains useful information
that opens new opportunities for content analysis on tweets. This paper
examines the predictive power of Twitter regarding the US presidential election
of 2012. For this study, we analyzed 32 million tweets regarding the US
presidential election by employing a combination of machine learning
techniques. We devised an advanced classifier for sentiment analysis in order
to increase the accuracy of Twitter content analysis. We carried out our
analysis by comparing Twitter results with traditional opinion polls. In
addition, we used the Latent Dirichlet Allocation model to extract the
underlying topical structure from the selected tweets. Our results show that we
can determine the popularity of candidates by running sentiment analysis. We
can also uncover candidates popularities in the US states by running the
sentiment analysis algorithm on geo-tagged tweets. To the best of our
knowledge, no previous work in the field has presented a systematic analysis of
a considerable number of tweets employing a combination of analysis techniques
by which we conducted this study. Thus, our results aptly suggest that Twitter
as a well-known social medium is a valid source in predicting future events
such as elections. This implies that understanding public opinions and trends
via social media in turn allows us to propose a cost- and time-effective way
not only for spreading and sharing information, but also for predicting future
events.
