ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (1): 224-232.doi: 10.7544/issn1000-1239.2021.20190305

Previous Articles    

Aspect Extraction Model Based on Interactive Feature Representation

Zeng Biqing1, Zeng Feng2, Han Xuli2,  Shang Qi2   

  1. 1(School of Software, South China Normal University, Foshan, Guangdong 528225);2(School of Computer Science, South China Normal University, Guangzhou 510631)
  • Online:2021-01-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61772211, 61503143).

Abstract: Aspect extraction is one of the key tasks in aspect level sentiment analysis, whose result will directly affect the accuracy of aspect level sentiment classification. In aspect extraction task, it is both time and labor consuming to enhance the performance of the model by handcraft features. Aiming at resolving the problems of insufficient data scale, insufficient feature information, etc., aspect extraction model based on interactive feature representation (AEMIFR) is proposed. Compared with other models, AEMIFR combines character level embedding and word embedding to capture the semantic features of words, the morphological features of characters and the internal relationship between characters and words. Furthermore, AEMIFR obtains the local feature representation and context-dependent feature representation of text, learns the interaction between the two feature representations, enhances the importance of similar features between the two feature representations, reduces the negative impact of useless features on the model, and learns higher quality feature representations. Finally experiments are conducted on the data sets L-14, R-14, R-15 and R-16 in SimEval 2014, SemEval 2015 and SemEval 2016, and the competitive effect is achieved.

Key words: aspect extraction, aspect level sentiment analysis, feature interaction, natural language processing, neural network

CLC Number: