Most sentiment analysis approaches rely on machine-learning techniques, using a bag-of-words (BoW) document representation as their basis. In this paper, we examine whether a more fine-grained representation of documents as sequences of emotionally-annotated sentences can increase document classification accuracy. Experiments conducted on a sentence and document level annotated corpus show that the proposed solution, combined with BoW features, offers an increase in classification accuracy.
['Georgios Paltoglou', 'Mike Thelwall']
More than Bag-of-Words: Sentence-based Document Representation for Sentiment Analysis