  Aspect-based opinion mining is the task of identifying sentiment at the
aspect level in opinionated text, which consists of two subtasks: aspect
category extraction and sentiment polarity classification. While aspect
category extraction aims to detect and categorize opinion targets such as
product features, sentiment polarity classification assigns a sentiment label,
i.e. positive, negative, or neutral, to each identified aspect. Supervised
learning methods have been shown to deliver better accuracy for this task but
they require labeled data, which is costly to obtain, especially for
resource-poor languages like Vietnamese. To address this problem, we present a
supervised aspect-based opinion mining method that utilizes labeled data from a
foreign language (English in this case), which is translated to Vietnamese by
an automated translation tool (Google Translate). Because aspects and opinions
in different languages may be expressed by different words, we propose using
word embeddings, in addition to other features, to reduce the vocabulary
difference between the original and translated texts, thus improving the
effectiveness of aspect category extraction and sentiment polarity
classification processes. We also introduce an annotated corpus of aspect
categories and sentiment polarities extracted from restaurant reviews in
Vietnamese, and conduct a series of experiments on the corpus. Experimental
results demonstrate the effectiveness of the proposed approach.
