期刊文献+

二次检索

题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

年份

共找到2篇文章
< 1 >
每页显示 20 50 100
Inheritance of Red Culture and Perception of Tourism Development in Yimeng under the Background of Cultural and Tourism Integration
1
作者 WANG Hui LIU Xiaomei +1 位作者 CHEN Lei MA Lin 《Journal of Landscape Research》 2023年第6期51-54,共4页
Developing red tourism is an important way to carry forward revolutionary culture and practice socialist core values.In this paper,effective comments on tourism websites such as“Ctrip”and“Tongcheng Travel”were sel... Developing red tourism is an important way to carry forward revolutionary culture and practice socialist core values.In this paper,effective comments on tourism websites such as“Ctrip”and“Tongcheng Travel”were selected as data sources,and with the help of network text analysis,the image perception and emotion of tourists in Linyi red tourism were analyzed.Besides,new ways to develop and utilize red tourism in Linyi City were put forward,such as innovating red tourism experiential products,promoting industrial linkage and common development,improving red tourism service facilities,and focusing on network marketing models,so as to reshape the red tourism value chain and enhance the comprehensive social effect. 展开更多
关键词 web text analysis Linyi Red tourism Image perception
下载PDF
Self-Switching Classification Framework for Titled Documents
2
作者 郭杭 周立柱 冯铃 《Journal of Computer Science & Technology》 SCIE EI CSCD 2009年第4期615-625,共11页
Ambiguous words refer to words that have multiple meanings such as apple, window. In text classification they are usually removed by feature reduction methods like Information Gain. Sometimes there are too many ambigu... Ambiguous words refer to words that have multiple meanings such as apple, window. In text classification they are usually removed by feature reduction methods like Information Gain. Sometimes there are too many ambiguous words in the corpus, which makes throwing away all of them not a viable option, as in the case when classifying documents from the Web. In this paper we look for a method to classify Titled documents with the help of ambiguous words. Titled documents are a kind of documents that have a simple structure containing a title and an excerpt. News, messages, and paper abstracts with titles are examples of titled documents. Instead of introducing another feature reduction method, we describe a framework to make the best use of ambiguous words in the titled documents. The framework improves the performance of a traditional bag-of-words classifier with the help of a bag-of-word-pairs classifier. The framework is implemented using one of the most popular classifiers, Multinomial NaiveBayes (MNB) as an example. The experiments with three real life datasets show that in our framework the MNB model performs much better than traditional MNB classifier and a naive weighted algorithm, which simply puts more weight on words in the title. 展开更多
关键词 text analysis machine learning web text analysis
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部