Deep learning and sub-tree mining for document level sentiment classification
| Autor: | Viet Anh Phan, Ngoc Phuong Chau, Minh-Le Nguyen |
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| Rok vydání: | 2016 |
| Předmět: |
Parsing
Relation (database) Computer science business.industry Deep learning Sentiment analysis Breadth-first search 020207 software engineering 02 engineering and technology Machine learning computer.software_genre 030507 speech-language pathology & audiology 03 medical and health sciences Tree (data structure) Outlier 0202 electrical engineering electronic engineering information engineering Artificial intelligence 0305 other medical science business computer Sentence |
| Zdroj: | KSE |
| DOI: | 10.1109/kse.2016.7758065 |
| Popis: | Recently, with the development of the online social network, sentiment classification (SC) which determines opinions of people is a significant task in natural language processing. In this research, we propose a model which combines deep learning and sub-tree mining to resolve sentiment classification problem. Stanford Parser is used to extract the relation from the beginning to the end of the sentences and each sentence is represented as a tree. Afterwards, FindBestSub-tree algorithm with sub-tree mining technique eliminates outliers in the dataset. Then, the order of the words in a sentence changes according to DFS (Depth First Search) from a tree after outlier removal phase. Finally, the association between all words in a sentence and all sentences in a document is captured by LSTM and GRNN, respectively. Document sentiment classification experiment is conducted on multi-domain sentiment dataset. The elimination of outliers leads to higher performance in this model1. In our experiment, the proposed method achieves improvements in term of accuracy in a range of 0.14% – 6.93% over LSTM + GRNN model. |
| Databáze: | OpenAIRE |
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