Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora

TL;DR

This paper presents a method to generate accurate domain-specific sentiment lexicons from unlabeled text using word embeddings and label propagation, enabling large-scale analysis of sentiment variation over time and across communities.

Contribution

It introduces a novel framework combining domain-specific embeddings with label propagation to induce sentiment lexicons without extensive manual curation.

Findings

Over 5% of sentiment words changed polarity in 150 years

Community-specific lexicons reveal drastic sentiment differences

Framework achieves state-of-the-art performance with minimal seed words

Abstract

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.