2017 Poster Sessions : Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora

Student Name : Will Hamilton
Advisor : Daniel Jurafsky
Research Areas: Artificial Intelligence
A word's sentiment depends on the domain in which it is used. Computational social science research thus requires sentiment lexicons that are specific to the domains being studied. We combine domain-specific word embeddings with a label propagation framework to induce accurate domain-specific sentiment lexicons using small sets of seed words, achieving state-of-the-art performance competitive with approaches that rely on hand-curated resources. Using our framework we perform two large-scale empirical studies to quantify the extent to which sentiment varies across time and between communities. We induce and release historical sentiment lexicons for 150 years of English and community-specific sentiment lexicons for 250 online communities from the social media forum Reddit. The historical lexicons show that more than 5% of sentiment-bearing (non-neutral) English words completely switched polarity during the last 150 years, and the community-specific lexicons highlight how sentiment varies drastically between different communities.

Will Hamilton is a 3rd-year CS PhD student working jointly with Prof Dan Jurafsky (NLP Group) and Prof Jure Leskovec (InfoLab). Will's work lies at the intersection of machine learning and computational social science. He develops computational methods that allow researchers to understand and predict the behavior of large social systems, with a focus on online communities.