Suggestion Mining from Online Reviews usingRandom Multimodel Deep Learning
Feng Liu, Liangji Wang, Xiaofeng Zhu, Dingding Wang · 2019
In this paper, we propose a new deep learning based method for suggestion mining. The major challenges of suggesion mining include cross domain issue and the issues caused by unstructured and highly imbalanced data structure. To overcome these challenges, we propose to apply Random Multimodel Deep Learning (RMDL) which combines three different deep learning architectures (DNNs, RNNs and CNNs) and automatically selects the optimal hyper parameter to improve the robustness and flexibility of the model. Our experimental results on the SemEval-2019 competition Task 9 data sets demonstrate that our proposed RMDL outperforms most of the existing suggestion mining methods.