Comparative Study of CNN and LSTM based Attention Neural Networks for Aspect-Level Opinion Mining
| Autor: | Xiaohua Tony Hu, Jianliang Gao, Zheng Chen, Wei Quan |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
Process (engineering) Computer science business.industry Deep learning Sentiment analysis Context (language use) 02 engineering and technology Semantics Machine learning computer.software_genre Convolutional neural network SemEval 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | IEEE BigData |
| DOI: | 10.1109/bigdata.2018.8622150 |
| Popis: | Aspect-level opinion mining aims to find and aggregate opinions on opinion targets. Previous work has demonstrated that precise modeling of opinion targets within the surrounding context can improve performances. However, how to effectively and efficiently learn hidden word semantics and better represent targets and the context still needs to be further studied. In this paper, we propose and compare two interactive attention neural networks for aspect-level opinion mining, one employs two bi-directional Long-Short-Term-Memory (BLSTM) and the other employs two Convolutional Neural Networks (CNN). Both frameworks learn opinion targets and the context respectively, followed by an attention mechanism that integrates hidden states learned from both the targets and context. We compare our model with state-of-the-art baselines on two SemEval 2014 datasets1. Experiment results show that our models obtain competitive performances against the baselines on both datasets. Our work contributes to the improvement of state-of-the-art aspect-level opinion mining methods and offers a new approach to support human decision-making process based on opinion mining results. The quantitative and qualitative comparisons in our work aim to give basic guidance for neural network selection in similar tasks. |
| Databáze: | OpenAIRE |
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