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 Bidirectional Independently Long Short-Term Memory and Conditional Random Field integrated model for Aspect Extraction in Sentiment Analysis
Tác giả hoặc Nhóm tác giả: Trang Tranuyen, Ha Hoangthithanh and Hiep Huynhxuan
Nơi đăng: In proceedings of the 7th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA 2018); Số: 7;Từ->đến trang: 79-88;Năm: 2018
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Aspect extraction or feature extraction is a crucial and challenging task of opinion mining that aims to identify opinion targets from opinion text. Especially, how to explore these aspects or features from unstructured comments is a matter of concern. In this paper, we propose a novel supervised learning approach using deep learning technique for the above mentioned aspect extraction task. Our model combines a Bidirectional Independently Long Short-Term Memory (Bi-IndyLSTM) with a Conditional Random Field (CRF). This integrated model is trained on labeled data to extract feature sets in opinion text. We employ a Bi-IndyLSTM with word embeddings achieved by training GloVe on the SemEval 2014 dataset. There are 6,086 training reviews and 1,600 testing reviews on two domains, Laptop and Restaurant of the SemEval 2014 dataset. Experimental results showed that our proposed Bi-IndyLSTM-CRF aspect extraction model in sentiment analysis obtained considerably better accuracy than the state-of-the-art methods.
ABSTRACT
Aspect extraction or feature extraction is a crucial and challenging task of opinion mining that aims to identify opinion targets from opinion text. Especially, how to explore these aspects or features from unstructured comments is a matter of concern. In this paper, we propose a novel supervised learning approach using deep learning technique for the above mentioned aspect extraction task. Our model combines a Bidirectional Independently Long Short-Term Memory (Bi-IndyLSTM) with a Conditional Random Field (CRF). This integrated model is trained on labeled data to extract feature sets in opinion text. We employ a Bi-IndyLSTM with word embeddings achieved by training GloVe on the SemEval 2014 dataset. There are 6,086 training reviews and 1,600 testing reviews on two domains, Laptop and Restaurant of the SemEval 2014 dataset. Experimental results showed that our proposed Bi-IndyLSTM-CRF aspect extraction model in sentiment analysis obtained considerably better accuracy than the state-of-the-art methods.
[ 2019\2019m06d019_21_0_2TRANUYENTRANG_Bi_Indy_LSTM_CRF2018.pdf ]
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