|Table of Contents|

Construction of Attention Mechanism Model of Urban Tourism Catering Experience:Deep Mining of Online Text Based on Machine Learning(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2022年01期
Page:
32-39
Research Field:
·地理学·
Publishing date:

Info

Title:
Construction of Attention Mechanism Model of Urban Tourism Catering Experience:Deep Mining of Online Text Based on Machine Learning
Author(s):
Zhang Xuhui1; 2; Zhang Chen1; 2; Li Yanan1; 2; Xu Ziyu1; 2; Huang Zhenfang1; 2
(1.School of Geography,Nanjing Normal University,Nanjing 210023,China)(2.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China)
Keywords:
tourism catering experience; Attention mechanism; machine learning; two factor theory; online text
PACS:
F592.7
DOI:
10.3969/j.issn.1001-4616.2022.01.006
Abstract:
The significance of a good tourist catering experience in enhancing the competitiveness of urban tourism cannot be underestimated. The tourist catering text records the real experience of tourists to the products and services of the catering shops in the tourist destination. The text has a large amount of content and is less constrained by time and space,which has certain analytical value. This paper integrates Attention mechanism with the convolutional neural network model in the machine learning method,and aims to deeply explore the deep semantic associations behind the catering text,and explore the motivational factors that affect tourists’ positive catering experience and the healthcare factors of negative catering experience and their influencing mechanisms,which are applicable to the characteristics of tourist catering texts themselves. Through the construction of attention mechanism model,it is found that the healthcare factors that affect tourists’ uation of Nanjing’s catering experience are composed of price,location,transportation and services,while the motivational factors are composed of taste,fame and cultural environment. In addition,this paper sorts the value of attention weight,and prioritizes the resolution of higher-weight factors to improve the catering experience of tourists.

References:

[1] 张海平,周星星,汤国安,等. 基于GIS场模型的城市餐饮服务热点探测及空间格局分析[J]. 地理研究,2020,39(2):354-369.
[2]秦萧,甄峰,朱寿佳,等. 基于网络口碑度的南京城区餐饮业空间分布格局研究——以大众点评网为例[J]. 地理科学,2014,34(7):810-817.
[3]李建州,白翠玲. 三类餐馆顾客体验实证研究[J]. 旅游学刊,2008,23(1):36-40.
[4]吴丽云,陈方英. 基于网络评论内容分析的餐饮消费者行为研究[J]. 人文地理,2015,30(5):147-152.
[5]蔡万坤. 中国餐饮市场的发展趋势及其对策[J]. 旅游学刊,2004,19(4):63-68.
[6]范秀成,李建州. 顾客餐馆体验的实证研究[J]. 旅游学刊,2006,21(3):56-61.
[7]张旭,徐逸伦. 南京市餐饮设施空间分布及其影响因素研究[J]. 热带地理,2009,29(4):362-367.
[8]李萍,陈田,王甫园,等. 基于文本挖掘的城市旅游社区形象感知研究——以北京市为例[J]. 地理研究,2017,36(6):1106-1122.
[9]于鹏,张宏梅,黄薇薇. 韩国旅游形象研究:基于韩国旅游发展局新浪微博的内容分析[J]. 安徽师范大学学报(自然科学版),2015,38(4):377-383.
[10]冯捷蕴. 北京旅游目的地形象的感知——中西方旅游者博客的多维话语分析[J]. 旅游学刊,2011,26(9):19-28.
[11]郭风华,王琨,张建立,等. 成都“五朵金花”乡村旅游地形象认知——基于博客游记文本的分析[J]. 旅游学刊,2015,30(4):84-94.
[12]CHENG M,XIN J. What do Airbnb users care about?An analysis of online review comments[J]. International journal of hospitality management,2019,76:58-70.
[13]KIM S,LEE W S. Network text analysis of medical tourism in newspapers using text mining:The South Korea case[J]. Tourism management perspectives,2019,31(1):332-339.
[14]周李,吴殿廷,虞虎,等. 基于网络游记的城市旅游流网络结构演化研究——以北京市为例[J]. 地理科学,2020,40(2):298-307.
[15]FANG B,YE Q,KUCUKUSTA D,et al. Analysis of the perceived value of online tourism reviews:influence of readability and reviewer characteristics[J]. Tourism management,2016,52:498-506.
[16]吴小根,杜莹莹. 旅游目的地游客感知形象形成机理与实证——以江苏省南通市为例[J]. 地理研究,2011,30(9):1554-1565.
[17]徐小波,赵磊,刘滨谊,等. 中国旅游城市形象感知特征与分异[J]. 地理研究,2015,34(7):1367-1379.
[18]彭丹,黄燕婷. 丽江古城旅游地意象研究:基于网络文本的内容分析[J]. 旅游学刊,2019,34(9):80-89.
[19]CELARDO L,EVERETT M G. Network text analysis:a two-way classification approach[J]. International journal of information management,2020,51:102009.
[20]刘逸,保继刚,陈凯琪. 中国赴澳大利亚游客的情感特征研究——基于大数据的文本分析[J]. 旅游学刊,2017,32(5):46-58.
[21]HU N,ZHANG T,GAO B,et al. What do hotel customers complain about? Text analysis using structural topic model[J]. Tourism management,2019,72:417-426.
[22]KIM K,PARK O J,YUN S,et al. What makes tourists feel negatively about tourism destinations?Application of hybrid text mining methodology to smart destination management[J]. Technological forecasting and social change,2017,123:362-369.
[23]AININ S,FEIZOLLAH A,ANUAR N B,et al. Sentiment analyses of multilingual tweets on halal tourism[J]. Tourism management perspectives,2020,34:100658.
[24]MANSOUR S. Social media analysis of user’s responses to terrorism using sentiment analysis and text mining[J]. Procedia computer science,2018,140:95-103.
[25]赵振斌,党娇. 基于网络文本内容分析的太白山背包旅游行为研究[J]. 人文地理,2011,26(1):134-139.
[26]GODNOV U,REDEK T. Application of text mining in tourism:case of Croatia[J]. Annals of tourism research,2016,58:162-166.
[27]张郴,黄震方,张捷,等. 基于机器学习的南京市旅游地个性及其文化景观表征[J]. 地理学报,2017,72(10):1886-1903.
[28]ZHANG C,HUANG Z F,CAO F,et al. Recognise me from outside to inside:learning the influence chain of urban destination personalities[J]. Tourism management,2019,70:390-403.
[29]张郴,黄震方. 旅游地三元空间交互理论模型建构[J]. 地理研究,2020,39(2):232-242.
[30]中国国家标准化管理委员会. 中华人民共和国国家标准:旅游业基础术语(GB/T 16766—2010)[S/OL]. [2021-08-06]. http://www.cnta.gov.cn.
[31]曾璇,崔海山,刘志根. 广州市餐饮店分布演变特征与影响因素[J]. 经济地理,2019,39(3):143-151.
[32]马斌斌,陈兴鹏,陈芳婷,等. 中华老字号企业空间分异及影响因素研究[J]. 地理研究,2020,39(10):2313-2329.
[33]KWON W,LEE M,BACK K J. Exploring the underlying factors of customer value in restaurants:a machine learning approach[J]. International journal of hospitality management,2020,91:102643.
[34]MIAO Q L,LI Q D,ZENG D. Fine-grained opinion mining by integrating multiple review sources[J]. Journal of the American society for information science and technology,2010,61(11):2288-2299.
[35]李君轶,任涛,陆路正. 游客情感计算的文本大数据挖掘方法比较研究[J]. 浙江大学学报(理学版),2020,47(4):507-520.

Memo

Memo:
-
Last Update: 1900-01-01