• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.2
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• pp.13-22
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• 2015

The study discovers mood state and enhancement effect of users by scenery of location targeting Seonyudo Park; where is widely recognized as the representative recycling environmental park as well as theme experience space and scenery admiration in Korea. Also, the influence level of park and thematic space upon wellness was researched for future park design and its base data. As a result of semantic differential(SD), the most items showed low point in positive way when people admiring the scenery in Seonyudo. Also, a subject experienced differently depending on each inside scenery element of the park. As a result of profile of mood states(POMS), a tension and anxiety points were shown in order of Urban (7.78) > Water Purification Basin(3.33) > Gardens of Water Plants(2.11) > Garden of Green Pillar(2.00) > Garden of Time (0.89). The depression points were shown in order of Urban(4.94) > Water Purification Basin(3.50) > Garden of Green Pillar(2.94) > Garden of Time(1.61) > Gardens of Water Plants(1.38). The anger and hostility points were shown in order of Urban(4.22) > Water Purification Basin(3.33) > Garden of Green Pillar(2.22) > Garden of Time(1.39) > Gardens of Water Plants(1.11). The fatigue points were shown in order of Urban(6.5) > Water Purification Basin(3.39) > Garden of Green Pillar(2.78) > Garden of Time(2.28) > Gardens of Water Plants (2.06). The vigor points were shown in order of Gardens of Water Plants(11.39) > Garden of Time(11.00) > Garden of Green Pillar(8.39) > Water Purification Basin(7.77) > Urban(5.28). Also, as a result of statistics analysis, difference value of scenery type is significant. The result of total emotional disturbance(TED) was analyzed in order of Urban(24.5) > Water Purification Basin(9.5) > Garden of Green Pillar(4.67) > Garden of Time(-1.39) > Gardens of Water Plants(-1.22).

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.97-113
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• 2016

Due to the growth of internet data and the rapid development of internet technology, "big data" analysis is actively conducted to analyze enormous data for various purposes. Especially in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of existing structured data analysis. Various studies on sentiment analysis, the part of text mining techniques, are actively studied to score opinions based on the distribution of polarity of words in documents. Usually, the sentiment analysis uses sentiment dictionary contains positivity and negativity of vocabularies. As a part of such studies, this study tries to construct sentiment dictionary which is customized to specific data domain. Using a common sentiment dictionary for sentiment analysis without considering data domain characteristic cannot reflect contextual expression only used in the specific data domain. So, we can expect using a modified sentiment dictionary customized to data domain can lead the improvement of sentiment analysis efficiency. Therefore, this study aims to suggest a way to construct customized dictionary to reflect characteristics of data domain. Especially, in this study, movie review data are divided by genre and construct genre-customized dictionaries. The performance of customized dictionary in sentiment analysis is compared with a common sentiment dictionary. In this study, IMDb data are chosen as the subject of analysis, and movie reviews are categorized by genre. Six genres in IMDb, 'action', 'animation', 'comedy', 'drama', 'horror', and 'sci-fi' are selected. Five highest ranking movies and five lowest ranking movies per genre are selected as training data set and two years' movie data from 2012 September 2012 to June 2014 are collected as test data set. Using SO-PMI (Semantic Orientation from Point-wise Mutual Information) technique, we build customized sentiment dictionary per genre and compare prediction accuracy on review rating. As a result of the analysis, the prediction using customized dictionaries improves prediction accuracy. The performance improvement is 2.82% in overall and is statistical significant. Especially, the customized dictionary on 'sci-fi' leads the highest accuracy improvement among six genres. Even though this study shows the usefulness of customized dictionaries in sentiment analysis, further studies are required to generalize the results. In this study, we only consider adjectives as additional terms in customized sentiment dictionary. Other part of text such as verb and adverb can be considered to improve sentiment analysis performance. Also, we need to apply customized sentiment dictionary to other domain such as product reviews.