Cécile Richart

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2016

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Datasets for Aspect-Based Sentiment Analysis in French
Marianna Apidianaki | Xavier Tannier | Cécile Richart
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Aspect Based Sentiment Analysis (ABSA) is the task of mining and summarizing opinions from text about specific entities and their aspects. This article describes two datasets for the development and testing of ABSA systems for French which comprise user reviews annotated with relevant entities, aspects and polarity values. The first dataset contains 457 restaurant reviews (2365 sentences) for training and testing ABSA systems, while the second contains 162 museum reviews (655 sentences) dedicated to out-of-domain evaluation. Both datasets were built as part of SemEval-2016 Task 5 “Aspect-Based Sentiment Analysis” where seven different languages were represented, and are publicly available for research purposes.
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