A Study of Travel Notes on Blogs based on Text Mining and Machine Learning
碩士 === 國立聯合大學 === 資訊管理學系碩士班 === 106 === Modern people are often under a variety of pressures. Traveling is a good and popular way for people to relieve their stresses. People usually collect information and plan their trips before setting out. Travel notes on blog are often regarded as important ref...
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ndltd-TW-106NUUM03960032019-05-16T00:30:08Z http://ndltd.ncl.edu.tw/handle/vee897 A Study of Travel Notes on Blogs based on Text Mining and Machine Learning 以文字探勘與機器學習法於部落格遊記之研究 Yeh, Shan-Yu 葉珊妤 碩士 國立聯合大學 資訊管理學系碩士班 106 Modern people are often under a variety of pressures. Traveling is a good and popular way for people to relieve their stresses. People usually collect information and plan their trips before setting out. Travel notes on blog are often regarded as important references. This study takes travel notes on blogs regarding Miaoli County as an example to analyze the relationships among attractions, restaurants and accommodations based on association rules, and presents rich information through simple graphics. In addition, a text mining approach is employed to analyze the contents and critical terms of the travel notes. Three machine learning methods including support vector machine, decision tree and backpropagation neural network, and one deep learning approach, deep neural network, are adopted to predict the amount of views and comments of travel notes on blogs. The results of this study can not only be used as references for tourists to plan their trips but also provide valuable information for bloggers to write more attractive travel notes to catch more peoples’ attentions. Li-Ching Ma 馬麗菁 2018 學位論文 ; thesis 68 zh-TW |
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碩士 === 國立聯合大學 === 資訊管理學系碩士班 === 106 === Modern people are often under a variety of pressures. Traveling is a good and popular way for people to relieve their stresses. People usually collect information and plan their trips before setting out. Travel notes on blog are often regarded as important references. This study takes travel notes on blogs regarding Miaoli County as an example to analyze the relationships among attractions, restaurants and accommodations based on association rules, and presents rich information through simple graphics. In addition, a text mining approach is employed to analyze the contents and critical terms of the travel notes. Three machine learning methods including support vector machine, decision tree and backpropagation neural network, and one deep learning approach, deep neural network, are adopted to predict the amount of views and comments of travel notes on blogs. The results of this study can not only be used as references for tourists to plan their trips but also provide valuable information for bloggers to write more attractive travel notes to catch more peoples’ attentions.
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Li-Ching Ma |
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Li-Ching Ma Yeh, Shan-Yu 葉珊妤 |
author |
Yeh, Shan-Yu 葉珊妤 |
spellingShingle |
Yeh, Shan-Yu 葉珊妤 A Study of Travel Notes on Blogs based on Text Mining and Machine Learning |
author_sort |
Yeh, Shan-Yu |
title |
A Study of Travel Notes on Blogs based on Text Mining and Machine Learning |
title_short |
A Study of Travel Notes on Blogs based on Text Mining and Machine Learning |
title_full |
A Study of Travel Notes on Blogs based on Text Mining and Machine Learning |
title_fullStr |
A Study of Travel Notes on Blogs based on Text Mining and Machine Learning |
title_full_unstemmed |
A Study of Travel Notes on Blogs based on Text Mining and Machine Learning |
title_sort |
study of travel notes on blogs based on text mining and machine learning |
publishDate |
2018 |
url |
http://ndltd.ncl.edu.tw/handle/vee897 |
work_keys_str_mv |
AT yehshanyu astudyoftravelnotesonblogsbasedontextminingandmachinelearning AT yèshānyú astudyoftravelnotesonblogsbasedontextminingandmachinelearning AT yehshanyu yǐwénzìtànkānyǔjīqìxuéxífǎyúbùluògéyóujìzhīyánjiū AT yèshānyú yǐwénzìtànkānyǔjīqìxuéxífǎyúbùluògéyóujìzhīyánjiū AT yehshanyu studyoftravelnotesonblogsbasedontextminingandmachinelearning AT yèshānyú studyoftravelnotesonblogsbasedontextminingandmachinelearning |
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