Semi-Supervised Generative Adversarial Network for Sentiment Analysis of
Sentiment analysis has become a very popular research topic and covers a
wide range of domains such as economy, politics and health. In the
pharmaceutical field, automated analysis of online user reviews provides
information on the effectiveness and potential side effects of drugs,
which could be used to improve pharmacovigilance systems. Deep learning
approaches have revolutionized the field of Natural Language Processing
(NLP), achieving state-of-the-art results in many tasks, such as
These methods require large annotated datasets to train their models.
However, in most real-world scenarios, obtaining high-quality labeled
datasets is an expensive and time-consuming task. In contrast, unlabeled
texts task can be, generally, easily obtained.
In this work, we propose a semi-supervised approach based on a
Semi-Supervised Generative Adversarial Network (SSGAN) to address the
lack of labeled data for the sentiment analysis of drug reviews, and
improve the results provided by supervised approaches in this task.
To evaluate the real contribution of this approach, we present a
benchmark comparison between our semi-supervised approach and a
supervised approach, which uses a similar architecture but without the
generative adversal setting.
Experimental results show better performance of the semi-supervised
approach when annotated reviews are less than ten percent of the
training set, obtaining a significant improvement for the classification
of neutral reviews, the class with least examples. To the best of our
knowledge, this is the first study that applies a SSGAN to the sentiment
classification of drug reviews. Our semi-supervised approach provides
promising results for dealing with the shortage of annotated dataset,
but there is still much room to improvement.