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
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
基金Sponsored by the Social Science Planning Research Project of Shandong Province(22CYMJ11)Key Project of Shandong Provincial Party Committee Party School(Shandong Administration College)+1 种基金Social Science Project of Tai’an City(22-YB-086)Key Project of Art Science in Shandong Province(L2023Z04190725).
文摘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.
基金supported by the National Natural Science Foundation of China under Grant Nos.60833003 and 60773156
文摘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.
