A Multi-lingual Annotated Dataset for Aspect-Oriented Opinion Mining

We present the Trip-MAML dataset, a Multi-Lingual dataset of hotel reviews that have been manually annotated at the sentence-level with Multi-Aspect senti-ment labels. This dataset has been built as an extension of an existent English-only dataset, adding documents written in Ital-ian and Spanish. We detail the dataset construction process, covering the data gathering, selection, and annotation. We present inter-annotator agreement figures and baseline experimental results, compar-ing the three languages. Trip-MAML is a multi-lingual dataset for aspect-oriented opinion mining that enables researchers (i) to face the problem on languages other than English and (ii) to the experiment the application of cross-lingual learning meth-ods to the task.