An Effective ELECTRA-Based Pipeline for Sentiment Analysis of Tourist Attraction Reviews
Source abstract
Abstract
In the era of information explosion, it is difficult for people to decide on a tourist destination quickly. Online travel review texts provide valuable references and suggestions to assist in decision making. However, tourist attraction reviews are primarily informal and noisy. Most works in this field focus on shallow machine learning models or non-pretrained deep learning models. These approaches struggle to generate satisfactory classification results. To solve this issue, the paper proposes a pipeline model. In the first step of this paper, we preprocess tourist attraction reviews by performing stopword removal, special character removal, redundancy deletion and negation substitution to reduce noise. Then, we propose an ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) classifier for sentiment analysis of tourist attraction review. Finally, we compare our pipeline model with several representative deep text classification models. Extensive experiments have demonstrated the effectiveness of our approach to sentiment analysis of tourist attraction reviews. We not only provide one high-quality dataset for tourist attraction reviews, but our work can also expand and promote the development of sentiment analysis in other domains.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sentiment Analysis and Opinion Mining · Digital Marketing and Social Media · Web Data Mining and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer and Control Engineering, Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou 350108, China
School of Computer Science and Electronic Engineering, University of Essex, Colchester CO2 8JT, UK
Funding
Central Leading Local Project “Fujian Mental Health Human-Computer Interaction Technology Research Center”
References (44)
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