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Fine-grained sentiment analysis oriented to product comment
LIU Li, WANG Yongheng, WEI Hang
Journal of Computer Applications
2015, 35 (12):
3481-3486.
DOI: 10.11772/j.issn.1001-9081.2015.12.3481
The traditional sentiment analysis is coarse-grained and ignores the comment targets, the existing fine-grained sentiment analysis ignores multi-target and multi-opinion sentences. In order to solve these problems, a method of fine-grained sentiment analysis based on Conditional Random Field (CRF) and syntax tree pruning was proposed. A parallel tri-training method based on MapReduce was used to label corpus autonomously. CRF model of integrating various features was used to extract positive/negative opinions and the target of opinions from comment sentences. To deal with the multi-target and multi-opinion sentences, syntax tree pruning was employed through building domain ontology and syntactic path library to eliminate the irrelevant target of opinions and extract the correct appraisal expressions. Finally, a visual product attribute report was generated. After syntax tree pruning, the accuracy of the proposed method on sentiment elements and appraisal expression can reach 89% approximately.The experimental results on two product domains of mobile phone and camera show that the proposed method outperforms the traditional methods on both sentiment analysis accuracy and training performance.
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