Political discourse in the United States is getting increasingly polarized. This polarization frequently causes different communities to react very differently to the same news events. Political blogs as a form of social media provide an unique insight into this phenomenon. We present a multitarget, semisupervised latent variable model, MCR-LDA to model this process by analyzing political blogs posts and their comment sections from different political communities jointly to predict the degree of polarization that news topics cause. Inspecting the model after inference reveals topics and the degree to which it triggers polarization. In this approach, community responses to news topics are observed using sentiment polarity and comment volume which serves as a proxy for the level of interest in the topic. In this context, we also present computational methods to assign sentiment polarity to the comments which serve as targets for latent variable models that predict the polarity based on the topics in the blog content. Our results show that the joint modeling of communities with different political beliefs using MCR-LDA does not sacrifice accuracy in sentiment polarity prediction when compared to approaches that are tailored to specific communities and additionally provides a view of the polarization in responses from the different communities.
We describe a unified and coherent syntactic framework for supporting a semantically-informed syntactic approach to statistical machine translation. Semantically enriched syntactic tags assigned to the target-language training texts improved translation quality. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English translation task. This finding supports the hypothesis (posed by many researchers in the MT community, e.g., in DARPA GALE) that both syntactic and semantic information are critical for improving translation quality---and further demonstrates that large gains can be achieved for low-resource languages with different word order than English.
Elections are a vital part of democracy allowing people to vote for the candidate they think can best lead the country. A candidate's campaign aims to demonstrate to the public why they think they are the best choice. However, in this age of constant media coverage and digital communications, the candidate is scrutinized at every step. A single misquote or negative news about a candidate can be the difference between him winning or losing the election. It becomes crucial to have a public relations manager who can guide and direct the candidate's campaign by prioritizing specific campaign activities. One critical aspect of the PR manager's work is to understand the public perception of their candidate and improve public sentiment about the candidate.
We seek to determine the effectiveness of using location-based social media to predict the outcome of the 2016 presidential election. To this aim, we create a dataset consisting of approximately 3 million tweets ranging from September 22nd to November 8th related to either Donald Trump or Hillary Clinton. Twenty-one states are chosen, with eleven categorized as swing states, five as Clinton favored and five as Trump favored. We incorporate two metrics in polling voter opinion for election outcomes: tweet volume and positive sentiment. Our data is labeled via a convolutional neural network trained on the sentiment140 dataset. To determine whether Twitter is an indicator of election outcome, we compare our results to the election outcome per state and across the nation. We use two approaches for determining state victories: winner-take-all and shared elector count. Our results show tweet sentiment mirrors the close races in the swing states; however, the differences in distribution of positive sentiment and volume between Clinton and Trump are not significant using our approach. Thus, we conclude neither sentiment nor volume is an accurate predictor of election results using our collection of data and labeling process.