Abstract
Aspect identification became an important task for aspect-based sentiment analysis. Previous approaches realized the importance of aspect identification in aspect-level sentiment analysis task. To this aim, there are different approaches proposed including rule-based and supervised learning based. Rule-based methods introduce rule mining based on features engineering, while supervised methods consider it as multi-task text classification problem. However, aspect identification is still a challenge from two perspectives: detecting the implicit aspect and mapping aspect-term into category. In this paper, we propose a novel neural network approach with Hint-embedding that aims at exploring the connection between an aspect and its semantic content in the sentence. Attention mechanism is designed to focus on different parts of a sentence based on aspects’ indicators. We experiment on benchmark datasets (SemEval 2014 task 4 restaurant and SemEval 2016 task 5 laptop), and results show that our model achieves considerable performance on aspect identification task.
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Acknowledgment
This work was supported by the Ministry of Science and Technology of China, National Key Research and Development Program (2016YFB1000703), NSF of China (61732014 and 61672432).
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Ahmed, M., Chen, Q., Wang, Y., Li, Z. (2019). Hint-Embedding Attention-Based LSTM for Aspect Identification Sentiment Analysis. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11671. Springer, Cham. https://doi.org/10.1007/978-3-030-29911-8_44
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