Abstract
Understanding cross-cultural differences has important implications for world affairs and many aspects of the life of society. Yet, the majority of text-mining methods to date focus on the analysis of monolingual texts. In contrast, we present a statistical model that simultaneously learns a set of common topics from multilingual, non-parallel data and automatically discovers the differences in perspectives on these topics across linguistic communities. We perform a behavioural evaluation of a subset of the differences identified by our model in English and Spanish to investigate their psychological validity.
Highlights
Recent years have seen a growing interest in textmining applications aimed at uncovering public opinions and social trends (Fader et al, 2007; Monroe et al, 2008; Gerrish and Blei, 2011; Pennacchiotti and Popescu, 2011)
We present a model that learns such common topics, while simultaneously identifying lexical features that are indicative of the underlying differences in perspectives on these topics by speakers of English, Spanish and Russian
We presented the first model that detects common topics from multilingual, non-parallel data and automatically uncovers differences in perspectives on these topics across linguistic communities
Summary
Recent years have seen a growing interest in textmining applications aimed at uncovering public opinions and social trends (Fader et al, 2007; Monroe et al, 2008; Gerrish and Blei, 2011; Pennacchiotti and Popescu, 2011). According to Thibodeau and Boroditsky, their results demonstrate that metaphors have profound influence on how we conceptualize and act with respect to societal issues This suggests that in order to gain a full understanding of social trends across populations, one needs to identify subtle but systematic linguistic differences that stem from the groups’ cultural backgrounds, expressed both literally and figuratively. Performing such an analysis by hand is labor-intensive and often impractical, in a multilingual setting where expertise in all of the languages of interest may be rare
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