• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of Distribution Science
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• v.10 no.1
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• pp.43-52
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• 2012

With the slowdown in environmental issues and the change of environmental consciousness, ecotourism is being discussed in various social fields. Ecotourism is being popularized for environmental protection, and now it is becoming a mainstream product from one of mass tourism. Ecotourism's emphasis on sustainable development in the tourism destination's society, economy, and environment, through ecotourism study and education, enable people to understand the core value of the ecological environment. 2011 was nominated as "the Year of World Forest" by the UN. In the recent years, forests are becoming increasingly important with their own values and functions in environment, economy, society, and culture. In particular, the global environmental issues caused by climate change are becoming an international agenda. Forests are the only effective solution for the carbon dioxide that causes global warming. Moreover, forests constitute a major part of ecotourism, and are now most used by ecotourists. For example, Korea, wherein 60% of the land is forest, attracts ecotourists. With the increasing interests in environment, the number of tourists visiting the ecosystem forest, which is highly valued for its conservation, is increasing significantly every year and is receiving considerable attention from the government. However, poor facilities in the forest ecotourism sites and improper market strategies are the reasons for the poor running of these sites. Furthermore, tourists' environmental awareness affects ecology environmental pollution or the optimization of forest ecotourism. In order to verify the relationships among tourist attractiveness, environmental consciousness, charm degrees of the attractions, and attitudes after tours, we established some scales based on existing research achievement. Then, using these scales, the researcher completed the questionnaire survey. From December 20, 2010 to February 20, 2011, after conducting surveys for 12 weeks, we finally obtained 582 valid questionnaires, from a total of 700 questionnaires, that could be used in statistical analysis. First, for the method of research and analysis, the researcher initially applied the Cronbach's (Alpha) for verifying the reliability, and subsequently applied the Exploratory factor analysis for verifying the validity. Second, in order to analyze the demographics, the researcher makes use of the Frequency analysis for the AMOS, measurement model, structural equation model computing, and also utilizes construct validity, convergent validity, discriminant validity, and nomological validity. Third, for the analysis of the ecotourists' environmental consciousness, impacts on tourist attractiveness, charm degrees of the attractions, and attitudes after the tour, the researcher uses AMOS 19, with the path analysis and equation of structure. After the research, researchers found that high awareness of natural protection lead to high tourist motivation and satisfaction and more positive attitude after the tour. Moreover, this research shows the psychological and behavioral reactions of the ecotourists to the ecotourist development. Accordingly, environmental consciousness does not affect the tourist attractiveness that has been interpreted as significant. Furthermore, people should focus on the change of natural protection consciousness and psychological reaction of ecotourists while ensuring the sustainable development of ecotourists and developing some ecotourist programs.