Taking sides: user classification for informal online political discourse

Research paper) Purpose To evaluate and extend existing natural language processing techniques into the domain of informal online political discussions. Design/methodology/approach A database of postings from a U.S. political discussion site was collected, along with self-reported political orientation data for the users. A variety of sentiment analysis, text classification, and social network analysis methods were applied to the postings and evaluated against the users’ self-descriptions. Findings Purely text-based methods performed poorly, but could be improved using techniques which took into account the users’ position in the online community. Research limitations The techniques we applied here are fairly simple, and more sophisticated learning algorithms may yield better results for text-based classification. Practical implications This work suggests that social network analysis is an important tool for performing natural language processing tasks with informal web texts. Originality/value This research extends sentiment analysis to a new subject domain (U.S. politics) and a new text genre (informal online discusssions). Taking sides: Graph-based user classification for informal online political discourse