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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (2/3): 207-213    DOI: 10.11925/infotech.2096-3467.2019.0678
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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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[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.

Key wordsCRF      LSTM      Attention Mechanism      Sentiment Analysis      Word2Vec     
Received: 14 June 2019      Published: 26 April 2020
ZTFLH:  TP391  
Corresponding Authors: Xue Fuliang     E-mail:

Cite this article:

Xue Fuliang,Liu Lifang. Fine-Grained Sentiment Analysis with CRF and ATAE-LSTM. Data Analysis and Knowledge Discovery, 2020, 4(2/3): 207-213.

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Research Framework
ATAE-LSTM Structure
Skip-gram Model
评论 Pos Tag 评论 Pos Tag
运行/ v B 也/ d O
速度/ n I 很/ d O
快/ a O 耐用/ a O
,/ x O x O
电池/ n B
Examples of Attribute Word Tags
The Trend of Euclidean Distance Under Different Clustering Number K
Results of Attribute Word Clustering
实验 P R F1
Experimental Result
属性面 属性词 正面情感 中性情感 负面情感
设计 设计 75% 0 25%
外形与功能 信号 4% 96% 0
相机 75% 0 25%
外形 89% 0 11%
摄像头 9% 0 91%
速度 充电速度 100% 0 0
系统速度 100% 0 0
Emotional Indicators of Some Attribute Surfaces
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