Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 595–605, Austin, Texas, November 1-5, 2016. c⃝2016 Association for Computational Linguistics Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora William L. Hamilton, Kevin Clark, Jure Leskovec, Dan Jurafsky Department of Computer Science, Stanford University, Stanford CA, 94305 wleif,kevclark,jure,jurafsky@stanford.edu 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 in- duce accurate domain-specific sentiment lex- icons using small sets of seed words, achiev- ing state-of-the-art performance competitive with approaches that rely on hand-curated re- sources. 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 communi- ties 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 dras- tically between different communities. 1 Introduction Inducing domain-specific sentiment lexicons is cru- cial to computational social science (CSS) research. Sentiment lexicons allow us to analyze key subjec- tive properties of texts like opinions and attitudes (Taboada et al., 2011). But lexical sentiment is hugely influenced by context. The word soft has a very different sentiment in an online sports commu- nity than it does in one dedicated to toy animals (Fig- ure 1). Terrific once had a highly negative conno- r/sports r/mylittlepony −6 −4 −2 420 6 Sentiment of soft Figure 1: The sentiment of soft in different online commu- nities. Sentiment values computed using SENTPROP (Section 3) on comments from Reddit communities illustrate how sen- timent depends on social context. Bootstrap-sampled standard deviations provide a measure of confidence with the scores. tation; now it is essentially synonymous with good (Figure 2). Without domain-specific lexicons, social scientific analyses can be misled by sentiment as- signments 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). Using experts or crowdsourcing to construct domain-specific sentiment lexicons is expensive and often time-consuming (Mohammad and Turney, 2010; Fast et al., 2016), and is especially prob- lematic when non-standard language (as in histori- cal documents or obscure social media forums) pre- vents annotators from understanding the sociolin- guistic context of the data. Web-scale sentiment lexicons can be automat- ically induced for large socially-diffuse domains, such as the internet-at-large (Velikovich et al., 2010) or all of Twitter (Tang et al., 2014). However, to study sentiment in domain-specific cases—financial documents, historical texts, or tight-knit social me- 595 dia forums—such generic lexicons may be inaccu- rate, and even introduce harmful biases (Loughran and McDonald, 2011).1 Researchers need a princi- pled and accurate framework for inducing lexicons that are specific to …

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