Abstract
This paper presents an analysis of the rating inference task – the task of correctly predicting the rating associated to a review, in the context of movie reviews. For achieving this objective, we study the use of automatic text summaries instead of the full reviews. An extrinsic evaluation framework is proposed, where full reviews and different types of summaries (positional, generic and sentiment-based) of several compression rates (from 10% to 50%) are evaluated. We are facing a difficult task; however, the results obtained are very promising and demonstrate that summaries are appropriate for the rating inference problem, performing at least equally to the full reviews when summaries are at least 30% compression rate. Moreover, we also find out that the way the review is organised, as well as the style of writing, strongly determines the performance of the different types of summaries.
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Saggion, H., Lloret, E., Palomar, M. (2012). Can Text Summaries Help Predict Ratings? A Case Study of Movie Reviews. In: Bouma, G., Ittoo, A., Métais, E., Wortmann, H. (eds) Natural Language Processing and Information Systems. NLDB 2012. Lecture Notes in Computer Science, vol 7337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31178-9_33
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DOI: https://doi.org/10.1007/978-3-642-31178-9_33
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