3.8 Article

Fine-Grained Emotion Analysis Based on Mixed Model for Product Review

出版社

ATLANTIS PRESS

关键词

emotional element detection; emotional tendency judgment; deep features; semantic clustering

资金

  1. Natural Science Foundation of Anhui Province [1508085QF119]
  2. State Key Program of National Natural Science of China [61432004]
  3. Open Project Program of the National Laboratory of Pattern Recognition (NLPR) [201407345]
  4. China Postdoctoral Science Foundation [2015M580532]
  5. National Natural Science Foundation of China [61461045]

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Nowadays, with the rapid development of B2C e-commerce and the popularity of online shopping, the Web storages huge number of product reviews comment by customers. A large number of reviews made it difficult for manufacturers or potential customers to track the comments and suggestions that customers made. This paper presents a method for extracting emotional elements containing emotional objects and emotional words and their tendencies from product reviews based on mixed model. First we constructed conditional random fields to extract emotional elements, lead-in semantic and word meaning as features to improve the robustness of feature template and used rules for hierarchical filtering errors. Then we constructed support vector machine to classify the emotional tendency of the fine-grained elements to achieve key information from product reviews. Deep semantic information imported based on neural network to improve the traditional bag of word model. Experimental results show that the proposed model with deep features efficiently improved the F-Measure.

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