Modeling language discrepancy for cross-lingual sentiment analysis

Language discrepancy is inherent and be part of human languages. Thereby, the same sentiment would be expressed in different patterns across different languages. Unfortunately, the language discrepancy is overlooked by existing works of cross-lingual sentiment analysis. How to accommodate the inherent language discrepancy in sentiment for better cross-lingual sentiment analysis is still an open question. In this paper, we aim to model the language discrepancy in sentiment expressions as intrinsic bilingual polarity correlations (IBPCs) for better cross-lingual sentiment analysis. Specifically, given a document of source language and its translated counterpart, we firstly devise a sentiment representation learning phase to extract monolingual sentiment representation for each document in this pair separately. Then, the two sentiment representations are transferred to be the points in a shared latent space, named hybrid sentiment space. The language discrepancy is then modeled as a fixed transfer vector under each particular polarity between the source and target languages in this hybrid sentiment space. Two relation-based bilingual sentiment transfer models (i.e., RBST-s, RBST-hp) are proposed to learn the fixped transfer vectors. The sentiment of a target-language document is then determined based on the transfer vector between it and its translated counterpart in the hybrid sentiment space. Extensive experiments over a real-world benchmark dataset demonstrate the superiority of the proposed models against several state-of-the-art alternatives.