Abstract
In this work, we propose an effective neural network architecture GABi-LSTM to address fine-grained financial target-dependent sentiment analysis from financial microblogs and news. We first adopt a gated mechanism to adaptively integrate character level and word level embeddings for word representation, then present an attention-based Bi-LSTM component to embed target-dependent information into sentence representation, and finally use a linear regression layer to predict sentiment score with respect to target company. Comparative experiments on financial benchmark datasets show that our proposed GABi-LSTM model outperforms baselines and previous top systems by a large margin and achieves the state-of-the-art performance.
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Acknowledgments
This work is supported by grants from Science and Technology Commission of Shanghai Municipality (14DZ2260800 and 15ZR1410700), Shanghai Collaborative Innovation Center of Trustworthy Software for Internet of Things (ZF1213) and NSFC (61402175).
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Jiang, M., Wang, J., Lan, M., Wu, Y. (2017). An Effective Gated and Attention-Based Neural Network Model for Fine-Grained Financial Target-Dependent Sentiment Analysis. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds) Knowledge Science, Engineering and Management. KSEM 2017. Lecture Notes in Computer Science(), vol 10412. Springer, Cham. https://doi.org/10.1007/978-3-319-63558-3_4
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