Sentiment Classification of Chinese Tourism Reviews Based on ERNIE-Gram+GCN

Senqi Yang; Xuliang Duan; Zeyan Xiao; Zhiyao Li; Yuhai Liu; Zhihao Jie; Dezhao Tang; Hui Du

doi:10.3390/ijerph192013520

Sentiment Classification of Chinese Tourism Reviews Based on ERNIE-Gram+GCN

Int J Environ Res Public Health. 2022 Oct 19;19(20):13520. doi: 10.3390/ijerph192013520.

Authors

Senqi Yang^{1

2}, Xuliang Duan^{1

2}, Zeyan Xiao^{1

2}, Zhiyao Li^{1

2}, Yuhai Liu^{1

2}, Zhihao Jie^{1

2}, Dezhao Tang^{1

2}, Hui Du³

Affiliations

¹ College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China.
² The Lab of Agricultural Information Engineering, Sichuan Key Laboratory, Ya'an 625000, China.
³ Housing and Urban-Rural Development Bureau of Lincheng County, Xingtai 054000, China.

Abstract

Nowadays, tourists increasingly prefer to check the reviews of attractions before traveling to decide whether to visit them or not. To respond to the change in the way tourists choose attractions, it is important to classify the reviews of attractions with high precision. In addition, more and more tourists like to use emojis to express their satisfaction or dissatisfaction with the attractions. In this paper, we built a dataset for Chinese attraction evaluation incorporating emojis (CAEIE) and proposed an explicitly n-gram masking method to enhance the integration of coarse-grained information into a pre-training (ERNIE-Gram) and Text Graph Convolutional Network (textGCN) (E2G) model to classify the dataset with a high accuracy. The E2G preprocesses the text and feeds it to ERNIE-Gram and TextGCN. ERNIE-Gram was trained using its unique mask mechanism to obtain the final probabilities. TextGCN used the dataset to construct heterogeneous graphs with comment text and words, which were trained to obtain a representation of the document output category probabilities. The two probabilities were calculated to obtain the final results. To demonstrate the validity of the E2G model, this paper was compared with advanced models. After experiments, it was shown that E2G had a good classification effect on the CAEIE dataset, and the accuracy of classification was up to 97.37%. Furthermore, the accuracy of E2G was 1.37% and 1.35% ahead of ERNIE-Gram and TextGCN, respectively. In addition, two sets of comparison experiments were conducted to verify the performance of TextGCN and TextGAT on the CAEIE dataset. The final results showed that ERNIE and ERNIE-Gram combined TextGCN and TextGAT, respectively, and TextGCN performed 1.6% and 2.15% ahead. This paper compared the effects of eight activation functions on the second layer of the TextGCN and the activation-function-rectified linear unit 6 (RELU6) with the best results based on experiments.

Keywords: natural language processing (NLP); scenic spot evaluation text; sustainable tourism; text classification; tourism satisfaction.

MeSH terms

China
Data Collection
Sentiment Analysis*
Tourism*

Grants and funding

This research received no funding.