@inproceedings{920b6871fcc9418a9a871240b6e54967,
title = "An effective gated and attention-based neural network model for fine-grained financial target-dependent sentiment analysis",
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.",
keywords = "Attention neural network, Financial domain, Gate mechanism, Stock market prediction, Target-dependent sentiment analysis",
author = "Mengxiao Jiang and Jianxiang Wang and Man Lan and Yuanbin Wu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 10th International Conference on Knowledge Science, Engineering and Management, KSEM 2017 ; Conference date: 19-08-2017 Through 20-08-2017",
year = "2017",
doi = "10.1007/978-3-319-63558-3\_4",
language = "英语",
isbn = "9783319635576",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "42--54",
editor = "Zili Zhang and Yong Ge and Zhi Jin and Gang Li and Michael Blumenstein",
booktitle = "Knowledge Science, Engineering and Management - 10th International Conference, KSEM 2017, Proceedings",
address = "德国",
}