基于方面情感三元组抽取的游客评论大数据细粒度情感分析

Fine-Grained Sentiment Analysis of Tourists’Reviews Big Data:Based on Aspect Sentiment Triplet Extraction

目前游客细粒度情感分析的研究多集中于基于主题聚类下的频次统计等的大致分析,缺乏更精准地从每一条评论中抽取所有属性情感特征的细致定量刻画。因此,文章基于国内四大知名旅游电商平台上有关贵州省数百家A级旅游景区的游客评论大数据,建构较大规模的旅游评论方面级情感分析(ABSA)中文标注数据集;采用基于BERT的LCF-AEPC方面情感三元组抽取联合模型,在近8万条主观评论文本集上端到端地实现17万余个方面情感三元组的预测输出;并基于此输出结果进行可视化量化分析,探究游客在景区不同方面以及在不同级别和不同类型景区上的情感特征差异及其原因,构建了包含四类要素的游客情感感知影响因素模型。文章通过人工智能领域的深度学习技术实现ABSA多任务可快速有效地捕捉游客对景区各方面的偏好、需求和意见等信息,将成为旅游领域细粒度情感研究的有益尝试和最新应用。

The current research on fine-grained sentiment analysis of tourists is mostly focused on approximate analysis based on topic clustering and frequency statistics and so on,lacking the meticulous quantitative portrayal of all aspect sentiment features extracted from each review more precisely.In this article,we constructed a large-scale ABSA Chinese annotated dataset of tourism reviews based on the big data of tourists’real reviews of hundreds of national A-level scenic spots in Guizhou Province on four wellknown domestic tourism e-commerce platforms.using BERT-based LCF-AEPC aspects sentiment triplet extraction joint model,the prediction output of more than 170,000 aspect sentiment triplets were realized end-to-end on a dataset of nearly 80,000 subjective review texts.Based on the output results,a visual and quantitative analysis was conducted to explore the differences in tourists’sentiment characteristic regarding different aspects of attractions,as well as across different levels and types of attractions.Then the reasons of the differences were tried to be explained and a framework of four-element tourist sentiment impact factors was constructed.This article utilizes the deep learning techniques in the field of artificial intelligence to achieve ABSA multi-task,thereby quickly and effectively capturing the information of tourists’preferences,needs,and opinions on various aspects of tourist attractions.This will be a beneficial attempt and the latest application of fine-grained sentiment research in the field of tourism.