  Suggestion mining is increasingly becoming an important task along with
sentiment analysis. In today's cyberspace world, people not only express their
sentiments and dispositions towards some entities or services, but they also
spend considerable time sharing their experiences and advice to fellow
customers and the product/service providers with two-fold agenda: helping
fellow customers who are likely to share a similar experience, and motivating
the producer to bring specific changes in their offerings which would be more
appreciated by the customers. In our current work, we propose a hybrid deep
learning model to identify whether a review text contains any suggestion. The
model employs semi-supervised learning to leverage the useful information from
the large amount of unlabeled data. We evaluate the performance of our proposed
model on a benchmark customer review dataset, comprising of the reviews of
Hotel and Electronics domains. Our proposed approach shows the F-scores of
65.6% and 65.5% for the Hotel and Electronics review datasets, respectively.
These performances are significantly better compared to the existing
state-of-the-art system.
