Exploiting Social Network Structure for Person-to-Person Sentiment Analysis

Person-to-person evaluations are prevalent in all kinds of discourse and important for es-tablishing reputations, building social bonds, and shaping public opinion. Such evaluations can be analyzed separately using signed so-cial networks and textual sentiment analysis, but this misses the rich interactions between language and social context. To capture such interactions, we develop a model that pre-dicts individual A’s opinion of individual B by synthesizing information from the signed social network in which A and B are embed-ded with sentiment analysis of the evaluative texts relating A to B. We prove that this prob-lem is NP-hard but can be relaxed to an ef-ficiently solvable hinge-loss Markov random field, and we show that this implementation outperforms text-only and network-only ver-sions in two very different datasets involving community-level decision-making: the Wiki-pedia Requests for Adminship corpus and the Convote U.S. Congressional speech corpus.