Jure Leskovec


2016

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Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora
William L. Hamilton | Kevin Clark | Jure Leskovec | Dan Jurafsky
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change
William L. Hamilton | Jure Leskovec | Dan Jurafsky
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
William L. Hamilton | Jure Leskovec | Dan Jurafsky
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Learning Linguistic Descriptors of User Roles in Online Communities
Alex Wang | William L. Hamilton | Jure Leskovec
Proceedings of the First Workshop on NLP and Computational Social Science

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Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health
Tim Althoff | Kevin Clark | Jure Leskovec
Transactions of the Association for Computational Linguistics, Volume 4

Mental illness is one of the most pressing public health issues of our time. While counseling and psychotherapy can be effective treatments, our knowledge about how to conduct successful counseling conversations has been limited due to lack of large-scale data with labeled outcomes of the conversations. In this paper, we present a large-scale, quantitative study on the discourse of text-message-based counseling conversations. We develop a set of novel computational discourse analysis methods to measure how various linguistic aspects of conversations are correlated with conversation outcomes. Applying techniques such as sequence-based conversation models, language model comparisons, message clustering, and psycholinguistics-inspired word frequency analyses, we discover actionable conversation strategies that are associated with better conversation outcomes.

2014

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Exploiting Social Network Structure for Person-to-Person Sentiment Analysis
Robert West | Hristo S. Paskov | Jure Leskovec | Christopher Potts
Transactions of the Association for Computational Linguistics, Volume 2

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 Wikipedia Requests for Adminship corpus and the Convote U.S. Congressional speech corpus.

2013

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A computational approach to politeness with application to social factors
Cristian Danescu-Niculescu-Mizil | Moritz Sudhof | Dan Jurafsky | Jure Leskovec | Christopher Potts
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)