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

This page refers to the following paper and provides supplementary proofs and a dataset:

Robert West, Hristo S. Paskov, Jure Leskovec, and Christopher Potts. Exploiting Social Network Structure for Person-to-Person Sentiment Analysis. In Transactions of the Association for Computational Linguistics, 2(Oct):297–310, 2014. [PDF]


Person-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion. Such evaluations can be analyzed separately using signed social 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 predicts individual A's opinion of individual B by synthesizing information from the signed social network in which A and B are embedded with sentiment analysis of the evaluative texts relating A to B. We prove that this problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss Markov random field, and we show that this implementation outperforms text-only and network-only versions in two very different datasets involving community-level decision-making: the Convote U.S. Congressional speech corpus and the Wikipedia Requests for Adminship corpus.

Dataset: Wikipedia Requests for Adminship (RfA)

Data is available from the Stanford Large Network Dataset Collection.

Additional proofs

Additional proofs can be found in this document.

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Last modified on October 07, 2014