Overcoming Language Variation in Sentiment Analysis with Social Attention

Overcoming Language Variation in Sentiment Analysis with Social Attention Yi Yang and Jacob Eisenstein School of Interactive Computing Georgia Institute of Technology Atlanta, GA 30308 {yiyang+jacobe}@gatech.edu Abstract Variation in language is ubiquitous, particu- larly in newer forms of writing such as social media. Fortunately, variation is not random; it is often linked to social properties of the au- thor. In this paper, we show how to exploit social networks to make sentiment analysis more robust to social language variation. The key idea is linguistic homophily: the tendency of socially linked individuals to use language in similar ways. We formalize this idea in a novel attention-based neural network architec- ture, in which attention is divided among sev- eral basis models, depending on the author’s position in the social network. This has the effect of smoothing the classification function across the social network, and makes it pos- sible to induce personalized classifiers even for authors for whom there is no labeled data or demographic metadata. This model signif- icantly improves the accuracies of sentiment analysis on Twitter and on review data. 1 Introduction Words can mean different things to different people. Fortunately, these differences are rarely idiosyn- cratic, but are often linked to social factors, such as age (Rosenthal and McKeown, 2011), gender (Eck- ert and McConnell-Ginet, 2003), race (Green, 2002), geography (Trudgill, 1974), and more inef- fable characteristics such as political and cultural attitudes (Fischer, 1958; Labov, 1963). In natural language processing (NLP), social media data has brought variation to the fore, spurring the develop- ment of new computational techniques for charac- terizing variation in the lexicon (Eisenstein et al., 2010), orthography (Eisenstein, 2015), and syn- tax (Blodgett et al., 2016). However, aside from the focused task of spelling normalization (Sproat et al., 2001; Aw et al., 2006), there have been few attempts to make NLP systems more robust to language vari- ation across speakers or writers. One exception is the work of Hovy (2015), who shows that the accuracies of sentiment analysis and topic classification can be improved by the inclusion of coarse-grained author demographics such as age and gender. However, such demographic informa- tion is not directly available in most datasets, and it is not yet clear whether predicted age and gen- der offer any improvements. On the other end of the spectrum are attempts to create personalized lan- guage technologies, as are often employed in infor- mation retrieval (Shen et al., 2005), recommender systems (Basilico and Hofmann, 2004), and lan- guage modeling (Federico, 1996). But personal- ization requires annotated data for each individual user—something that may be possible in interactive settings such as information retrieval, but is not typ- ically feasible in natural language processing. We propose a middle ground between group-level demographic characteristics and personalization, by exploiting social network structure. The sociologi- cal theory of homophily asserts that individuals are usually similar to their friends (McPherson et al., 2001). This property has been demonstrated for lan- guage (Bryden et al., 2013) as well as for the demo- …

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