  Online services are interested in solutions to opinion mining, which is the
problem of extracting aspects, opinions, and sentiments from text. One method
to mine opinions is to leverage the recent success of pre-trained language
models which can be fine-tuned to obtain high-quality extractions from reviews.
However, fine-tuning language models still requires a non-trivial amount of
training data. In this paper, we study the problem of how to significantly
reduce the amount of labeled training data required in fine-tuning language
models for opinion mining. We describe Snippext, an opinion mining system
developed over a language model that is fine-tuned through semi-supervised
learning with augmented data. A novelty of Snippext is its clever use of a
two-prong approach to achieve state-of-the-art (SOTA) performance with little
labeled training data through: (1) data augmentation to automatically generate
more labeled training data from existing ones, and (2) a semi-supervised
learning technique to leverage the massive amount of unlabeled data in addition
to the (limited amount of) labeled data. We show with extensive experiments
that Snippext performs comparably and can even exceed previous SOTA results on
several opinion mining tasks with only half the training data required.
Furthermore, it achieves new SOTA results when all training data are leveraged.
By comparison to a baseline pipeline, we found that Snippext extracts
significantly more fine-grained opinions which enable new opportunities of
downstream applications.
