A User-based Approach to Multi-way Polarity Classification

Sentiment analysis deals with inferring people’s sentiments and opinions from texts. An important aspect of sentiment analysis is polarity classification, which consists of inferring a document’s polarity – the overall sentiment conveyed by the text – in the form of a numerical rating. In contrast to existing approaches to polarity classification, we propose to take the authors of the documents into account. Specifically, we present a nearest-neighbour collaborative approach that utilises novel models of user similarity. Our evaluation shows that our approach improves on state-of-the-art performance in terms of classification error and runtime, and yields insights regarding datasets for which such an improvement is achievable.