@inproceedings{SisLab4180,
       booktitle = {The 12th IEEE International Conference on KNOWLEDGE AND SYSTEMS ENGINEERING ( KSE 2020)},
           month = {November},
           title = {A Targeted Topic Model based Multi-Label 
Deep Learning Classification Framework 
for Aspect-based Opinion Mining},
          author = {Thi Cham Nguyen and Thi Ngan Pham and Hoang Quynh Le and Tri Thanh Nguyen and Hong Nhung Bui and Quang Thuy Ha},
            year = {2020},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4180/},
        abstract = {Recently, deep Convolutional Neural Network (CNN) model has achieved remarkable results in Natural Language Processing (NLP) tasks, such as information retrieval, relation classification, semantic parsing, sentence modeling and other traditional NLP tasks, etc. On the other hand, topic modeling method has been proved to be effective by exploiting hidden knowledge in a corpus of documents. Motivated from these successes, we propose a framework that takes advantages of closure domain measure to get enriched knowledge from close domains to the dataset of the current task to improve the CNN model, and apply a Targeted Topic Model to take more detailed exploration on each labeled aspect of an opinion. Experimental results on different scenarios show the effectiveness of the proposed framework for multi-label classification task in comparison to other related models on the same Hotel review dataset.}
}