Opinion aspect extraction in Dutch childrens diary entries
This addresses aspect extraction for Dutch children's language, which is incremental as it adapts an existing method to a new domain.
The study tackled aspect extraction in Dutch children's diary entries by creating a new annotated dataset and training a GRU model on Dutch restaurant reviews, achieving state-of-the-art performance on the review dataset and promising results on the children's dataset.
Aspect extraction can be used in dialogue systems to understand the topic of opinionated text. Expressing an empathetic reaction to an opinion can strengthen the bond between a human and, for example, a robot. The aim of this study is three-fold: 1. create a new annotated dataset for both aspect extraction and opinion words for Dutch childrens language, 2. acquire aspect extraction results for this task and 3. improve current results for aspect extraction in Dutch reviews. This was done by training a deep learning Gated Recurrent Unit (GRU) model, originally developed for an English review dataset, on Dutch restaurant review data to classify both opinion words and their respective aspects. We obtained state-of-the-art performance on the Dutch restaurant review dataset. Additionally, we acquired aspect extraction results for the Dutch childrens dataset. Since the model was trained on standardised language, these results are quite promising.