Graph-Based Semi-supervised Learning for Cross-Lingual Sentiment Classification

Graph-Based Semi-supervised Learning for Cross-Lingual Sentiment Classification
Mohammad Sadegh Hajmohammadi;
Roliana Ibrahim;
Ali Selamat
Cross-lingual sentiment classification aims to use labeled sentiment data in one language. Most existing research works rely on autpmatic machine translation services to directly transfer information from one language to another. How ever, different term distribution between translated data and original data can lead to low performance in cross-lingual sentiment classification. Further, due to the existence of differing structures and writing styles between different languages, using only information of labeled data from a different language cannot show a good performance in this classification task. To overcome these problems, we propose a new model which uses sentiment information of unlabelled data as well as labeled data in a graph-based semi-supervised learning approach so as to incorporate intrinsic structure of unlabelled data from the target language into the learning process. The proposed model was applied to book review datasets in two different languages. Experiments have shown that our model can effectively improve the cross-lingual sentiment classification performance in comparison with some baseline methods.
Sentiment classification;
Semi-supervised learning
Corresponding Author:
Intelligent Information and Database Systems