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In aspect-based sentiment analysis, most existing methods either focus onaspect/opinion terms extraction or aspect terms categorization. However, eachtask by itself only provides partial information to end users. To generate moredetailed and structured opinion analysis, we propose a finer-grained problem,which we call category-specific aspect and opinion terms extraction. Thisproblem involves the identification of aspect and opinion terms within eachsentence, as well as the categorization of the identified terms. To this end,we propose an end-to-end multi-task attention model, where each taskcorresponds to aspect/opinion terms extraction for a specific category. Ourmodel benefits from exploring the commonalities and relationships amongdifferent tasks to address the data sparsity issue. We demonstrate itsstate-of-the-art performance on three benchmark datasets.

Multi-task memory networks for category-specific aspect and opinion terms co-extraction