A System for Multilingual Sentiment Learning On Large Data Sets

Classifying documents according to the sentiment they convey (whether positive or negative) is an important problem in computational linguistics. There has not been much work done in this area on general techniques that can be applied effectively to multiple languages, nor have very large data sets been used in empirical studies of sentiment classifiers. We present an empirical study of the effectiveness of several sentiment classification algorithms when applied to nine languages (including Germanic, Romance, and East Asian languages). The algorithms are implemented as part of a system that can be applied to multilingual data. We trained and tested the system on a data set that is substantially larger than that typically encountered in the literature. We also consider a generalization of the n-gram model and a variant that reduces memory consumption, and evaluate their effectiveness.