  Aspect-based sentiment analysis of review texts is of great value for
understanding user feedback in a fine-grained manner. It has in general two
sub-tasks: (i) extracting aspects from each review, and (ii) classifying
aspect-based reviews by sentiment polarity. In this paper, we propose a
weakly-supervised approach for aspect-based sentiment analysis, which uses only
a few keywords describing each aspect/sentiment without using any labeled
examples. Existing methods are either designed only for one of the sub-tasks,
neglecting the benefit of coupling both, or are based on topic models that may
contain overlapping concepts. We propose to first learn <sentiment, aspect>
joint topic embeddings in the word embedding space by imposing regularizations
to encourage topic distinctiveness, and then use neural models to generalize
the word-level discriminative information by pre-training the classifiers with
embedding-based predictions and self-training them on unlabeled data. Our
comprehensive performance analysis shows that our method generates quality
joint topics and outperforms the baselines significantly (7.4% and 5.1%
F1-score gain on average for aspect and sentiment classification respectively)
on benchmark datasets. Our code and data are available at
https://github.com/teapot123/JASen.
