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Hint-Embedding Attention-Based LSTM for Aspect Identification Sentiment Analysis

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PRICAI 2019: Trends in Artificial Intelligence (PRICAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11671))

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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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Notes

  1. 1.

    Pre-trained model of GloVe is available from stanford.

  2. 2.

    Tool for data visualization, it is available on plotly.

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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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Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi, People’s Republic of China

    Murtadha Ahmed, Qun Chen, Yanyan Wang & Zhanhuai Li

  2. Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an, Shaanxi, People’s Republic of China

    Murtadha Ahmed, Qun Chen, Yanyan Wang & Zhanhuai Li

Authors
  1. Murtadha Ahmed
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  2. Qun Chen
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  3. Yanyan Wang
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  4. Zhanhuai Li
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Corresponding authors

Correspondence to Murtadha Ahmed , Qun Chen , Yanyan Wang or Zhanhuai Li .

Editor information

Editors and Affiliations

  1. Department of Computing, Macquarie University, Sydney, NSW, Australia

    Abhaya C. Nayak

  2. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Alok Sharma

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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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  • DOI: https://doi.org/10.1007/978-3-030-29911-8_44

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-29911-8

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