Product feature sentiment analysis based on GRU-CAP considering Chinese sarcasm recognition

Abstract

Sentiment analysis based on product online reviews is an important measure in grasping customer’s satisfaction with products, which has the problem of inadequate feature extraction and difficulty in identifying the sentiment polarity of texts with irony. To solve the problem, this paper proposes a novel method of product feature sentiment analysis based on gate recurrent units and capsule neural network (GRU-CAP). Firstly, product online reviews are preprocessed to build a review data set. The feature extraction model of GRU-CAP is then used to extract the product features contained in the data set and generate the corresponding product feature index. After combining the feature index with the data set to form new input data, the recognition of positive/negative emotions, sarcastic/non-sarcastic semantics is carried out through the sentiment analysis model of GRU-CAP. Finally, the recognition results are integrated to achieve fine-grained product feature sentiment analysis. The effectiveness and practicability of the method is validated by an example product-mobile phone on an e-commerce platform. The results show that the method can extract product features and identify sentiment polarity from product online review texts with irony more accurately, so as to provide better product design support for enhancing customer satisfaction with products more effectively.