Merged agreement algorithms for domain independent sentiment analysis
Résumé
In this paper, we consider the problem of building models that have high sentiment classification accuracy across domains. For that purpose, we present and evaluate three new algorithms based on multi-view learning using both high-level and low-level views, which show improved results compared to the state-of-the-art SAR algorithm [1] over cross-domain text subjectivity classification. Our experimental results present accuracy levels of 80% with two views, combining SVM classifiers over high-level features and unigrams compared to 77.1% for the SAR algorithm.