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| Fine-Grained Sentiment Analysis with CRF and ATAE-LSTM |
Xue Fuliang( ),Liu Lifang |
| Business School, Tianjin University of Finance & Economics, Tianjin 300222, China |
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Abstract [Objective] This paper tries to extract product attributes, aiming to cluster these words and analyze user’s sentiments.[Methods] Firstly, we identified the attributes of products with CRF technique. Then, we analyzed the sentiment of extracted terms with attention-based LSTM. Finally, we clustered these terms into appropriate categories with the help of Word2Vec and conducted fine-grained sentiment analysis of the products.[Results] The F1 values of term extraction and sentiment analysis were 0.76 and 0.78.[Limitations] We only retrieved explicit terms for this study and the sample size needs to be expanded.[Conclusions] The proposed method could effectively explore user’s preference in products.
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Received: 14 June 2019
Published: 26 April 2020
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Corresponding Authors:
Xue Fuliang
E-mail: fuliangxue@163.com
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