期刊
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
卷 10, 期 8, 页码 2163-2175出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-018-0799-4
关键词
Aspect-based sentiment analysis; Deep learning; Recurrent neural network; Long short-term memory
资金
- Jordan University of Science and Technology [20150164]
This paper proposes a state-of-the-art research for aspect-based sentiment analysis of Arabic Hotels' reviews using two implementations of long short-term memory (LSTM) neural networks. The first one is (a) a character-level bidirectional LSTM along with conditional random field classifier (Bi-LSTM-CRF) for aspect opinion target expressions (OTEs) extraction, and the second one is (b) an aspect-based LSTM for aspect sentiment polarity classification in which the aspect-OTEs are considered as attention expressions to support the sentiment polarity identification. Proposed approaches are evaluated using a reference dataset of Arabic Hotels' reviews. Results show that our approaches outperform baseline research on both tasks with an enhancement of 39% for the task of aspect-OTEs extraction and 6% for the aspect sentiment polarity classification task.
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