Collaborative inference of sentiments from texts

Authors:
Yanir Seroussi;Ingrid Zukerman;Fabian Bohnert
Affiliations:
Faculty of Information Technology, Monash University, Clayton, Victoria, Australia;Faculty of Information Technology, Monash University, Clayton, Victoria, Australia;Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
Venue:
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Year:
2010

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Abstract

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, and yields insights regarding datasets for which such an improvement is achievable.