Abstract
In this paper we address a novel sentiment analysis task of rating inference. Previous rating inference tasks, which are sometimes referred to as “seeing stars”, estimate only one rating in a document. However reviewers judge not only the overall polarity for a product but also details for it. A document in this new task contains several ratings for a product. Furthermore the range of the ratings is zero to six points (i.e., stars). In other words this task denotes “seeing several stars in a document”. If significant words or phrases for evaluation criteria and their strength as positive or negative opinions are extracted, a system with the knowledge can recommend products for users appropriately. For example, the system can output a detailed summary from review documents. In this paper we compare several methods to infer the ratings in a document and discuss a feature selection approach for the methods. The experimental results are useful for new researchers who try this new task.
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Shimada, K., Endo, T. (2008). Seeing Several Stars: A Rating Inference Task for a Document Containing Several Evaluation Criteria. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_106
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DOI: https://doi.org/10.1007/978-3-540-68125-0_106
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