Abstract
Target-dependent sentiment analysis is a fine-grained sentiment analysis and has received an increasing attention. For target-dependent sentiment analysis, the key issue is to capture the important context information according to the given target word. While some critical information in the context may be in a long distance from the target word, so it is significant to explore how to adequately and directly capture these long-range information. The dependency relation can connect words which are relevant in syntax but far in word order. Inspired by this, we propose Dependency-Attention-based Long Short-Term Memory Network (DAT-LSTM) and Segmented Dependency-Attention-based Long Short-Term Memory Network (Seg-DAT-LSTM) for target-dependent sentiment analysis. The dependency-attention mechanism utilizes dependency relation to fully capture long-range information for certain target. Experiments on the tweet dataset and SemEval 2014 dataset indicate that our models achieve state-of-the-art performance on target-dependent sentiment classification.
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Wang, X., Chen, G. (2017). Dependency-Attention-Based LSTM for Target-Dependent Sentiment Analysis. In: Cheng, X., Ma, W., Liu, H., Shen, H., Feng, S., Xie, X. (eds) Social Media Processing. SMP 2017. Communications in Computer and Information Science, vol 774. Springer, Singapore. https://doi.org/10.1007/978-981-10-6805-8_17
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DOI: https://doi.org/10.1007/978-981-10-6805-8_17
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