Abstract:
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.