A Targeted Topic Model based Multi-Label Deep Learning Classification Framework for Aspect-based Opinion Mining
| Autor: | Thi-Ngan Pham, Thi-Cham Nguyen, Hong-Nhung Bui, Quang-Thuy Ha, Hoang-Quynh Le, Tri-Thanh Nguyen |
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| Rok vydání: | 2020 |
| Předmět: |
Topic model
Word embedding Parsing business.industry Computer science Deep learning Sentiment analysis computer.software_genre Convolutional neural network Domain (software engineering) ComputingMethodologies_PATTERNRECOGNITION Artificial intelligence business computer Sentence Natural language processing |
| Zdroj: | KSE |
| Popis: | 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 the advantages of closure domain measure to get enriched knowledge from close domains to the training dataset 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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