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. We show that our approach achieves state-of-the-art performance on inducing sentiment lexicons from domain-specific corpora and that our purely corpus-based approach outperforms methods that rely on hand-curated resources (e.g., WordNet). Using our framework, 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 we induce 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.
Highlights
Inducing domain-specific sentiment lexicons is crucial to computational social science (CSS) research
Overall our results show that SENTPROP— a relatively simple method, which combines high-quality word vectors embeddings with standard label propagation — can perform at a state-of-the-art level, even performing competitively with methods relying on hand-curated lexical graphs
In cases where very large corpora are available and where there is an abundance of training data, DENSIFIER performs extremely well, since it was designed for this sort of setting (Rothe et al, 2016)
Summary
Inducing domain-specific sentiment lexicons is crucial to computational social science (CSS) research. Sentiment lexicons allow us to analyze key subjective properties of texts like opinions and attitudes (Taboada et al, 2011). Lexical sentiment is hugely influenced by context. The word soft has a very different sentiment in an online sports community than it does in one dedicated to toy animals (Figure 1). Terrific once had a highly negative conno-. 6 4 2 0 −2 −4 −6 r/sports r/mylittlepony tation; it is essentially synonymous with good (Figure 2). Without domain-specific lexicons, social scientific analyses can be misled by sentiment assignments biased towards domain-general contexts, neglecting factors like genre, community-specific vernacular, or demographic variation (Deng et al, 2014; Hovy, 2015; Yang and Eisenstein, 2015)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
