• Advanced Industrial SCIence
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• v.3 no.2
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• pp.1-7
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• 2024

This paper deals with the topic of quantification of soft power through emotional analysis. Sentiment analysis refers to the process of detecting and analyzing emotions or emotions in various data such as text, voice, and images. Therefore, in this paper, we explored the methodology and significance of how soft power can be quantified through emotional analysis. Soft power refers to the ability of a country or organization to influence the behavior of another country or organization in a desired direction. It is built by soft factors such as culture, values, and political system rather than military or economic means. Additionally, sentiment analysis is being used as a useful tool to measure and understand these soft areas.

• Journal of the Korean Society of Clothing and Textiles
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• v.37 no.7
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• pp.984-999
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• 2013

This research examines the interrelation of clothes, colors and sentiments based on clothes and colors that stimulate sentiment. This study provides data that is useful to color therapy by means of clothes as medium. The survey for this study targeted 200 Pusan National University students who analyzed the colors of association and clothes colors for nine positive vocabularies (passion, love, warmth, happiness, interest, softness, comfortable, freshness, and coolness) and six negative vocabularies (anger, fear, despair, nervous, gloomy, and loneliness). The data collection process used 120 standard colors as represented by Munsell's basic 10 colors (R, YR, Y, GY, G, BG, B, PB, P, RP) as chromatic colors classified into eleven tones of colors (V, S, B, P, VP, LGR, GR, L, DL, DP, DK) and achromatic colors divided into ten steps of brightness N1-N10. The results of the research are as follows. First, the warm class of colors were significant in the colors of association with positive sentiment and the cold class of colors were significant in the sentiment of refreshment and coolness. In addition, bright and clear colors (like V, S, VP, P) were associated with color tones. Second, the low bright achromatic colors were generally high for the colors of association with negative sentiment; in addition, the color of R, PB, P (as achromatic colors) were also significant. In addition, sober and dark tones (like GR, DL, DK, DP) were significant in color tones. Third, the interrelation between positive sentiment and clothes colors shows that colors of association were mainly used for upper garment colors. Similar colors against upper garments were used together for bottom garment achromatic colors and complementary colors; therefore, bottom garments play a subsidiary role in the concept of coordination with upper garments.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.254-262
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• 2009

As the trend of e-business activities develop, customers come into contact with products through on-line shopping sites and lots of customers refer product reviews before the purchasing on-line. However, as the volume of product reviews grow, it takes a great deal of time and effort for customers to read and evaluate voluminous product reviews. Lately, attention is being paid to Opinion Mining(OM) as one of the effective solutions to this problem. In this paper, we propose an efficient method for opinion sentiment classification of product reviews using product specific context information of words occurred in the reviews. We define the context information of words and propose the application of context for sentiment classification and we show the performance of our method through the experiments. Additionally, in case of word corpus construction, we propose the method to construct word corpus automatically using the review texts and review scores in order to prevent traditional manual process. In consequence, we can easily get exact sentiment polarities of opinion words in product reviews.

• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.383-391
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• 2017

This study examines the relationship between sentiment of speculators and price movements in the futures markets of WTI crude oil, copper, and wheat during the period 2003~2014 using Granger causality tests. The results indicate that speculative positions overall has no predictive power for returns in each futures market. Rather, returns seem to have effects on speculators' sentiment especially during periods of both economic expansion and recovery. During a recession, meanwhile, changes of speculators' sentiment index in the WTI crude oil and copper markets provide predictive power for returns in a positive direction, suggesting that speculators' pessimistic sentiment aggravates declines in commodity prices. Since the effects of speculative positions on market prices are ambiguous, tight regulations on speculative trading are not advisable. In a bearish market, however, regulatory bodies should consider raising speculative position limits because large speculative short positions and (or) liquidation of index traders' long positions may lead steep price declines.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.1-7
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• 2019

With the in-depth development of machine learning, the deep learning method has made great progress, especially with the Convolution Neural Network(CNN). Compared with traditional text sentiment classification methods, deep learning based CNNs have made great progress in text classification and processing of complex multi-label and multi-classification experiments. However, there are also problems with the neural network for text sentiment classification. In this paper, we propose a fusion model based on Long-Short Term Memory networks(LSTM) and CNN deep learning methods, and applied to multi-category news datasets, and achieved good results. Experiments show that the fusion model based on deep learning has greatly improved the precision and accuracy of text sentiment classification. This method will become an important way to optimize the model and improve the performance of the model.

• Korean Journal of Cognitive Science
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• v.19 no.4
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• pp.499-517
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• 2008

In this paper, we propose an effective emotion feature extraction method for Korean and evaluate their availability in sentiment classification. Korean emotion features are expanded from several representative emotion words and they play an important role in building in an effective sentiment classification system. Firstly, synonym information of English word thesaurus is used to extract effective emotion features and then the extracted English emotion features are translated into Korean. To evaluate the extracted Korean emotion features, we represent each document using the extracted features and classify it using SVM(Support Vector Machine). In experimental results, the sentiment classification system using the extracted Korean emotion features obtained more improved performance(14.1%) than the system using content-words based features which have generally used in common text classification systems.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.249-259
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• 2020

This study presents a recently obtained social media data set based upon the case study of Electric Vehicles (EV) and looks to implement a sentiment analysis (SA) in order to gain insights. This study uses two methods in order to fully analyze the public's sentiment on EVs. First, we implement a SA tool in which we used to extract the sentiment of comments. Next we labeled the data with these sentiments obtained and classified them. While performing classification we found the problem of dimensionality and also explored the use of feature selection (FS) models in order to reduce the data set's dimensionality. We found that the use of three FS models (Chi Squared, Information Gain and ReliefF) showed the most promising results when used alongside a logistic and support vector machines classification algorithm. the contributions of this paper are in providing an real-world example of social media text analytics which can be adopted in many other areas of research and business. Moving forward researchers can use the methodological approach in this paper to further refine and improve their own case uses in text analytics.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.462-472
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• 2014

As the Rope Type Platform Safety Door (RPSD) is now commercially available, the technology of RSPD and the public sentiment towards RPSD are being scrutinized. During the period of RPSD development and trial installation, there has been a need to examine its technical reliability and safety, and its users' emotional attitudes. Though often dichotomized in practice, technological innovation of, and the public sentiment towards RPSD are directly related to continuing and collaborated efforts to enhance public satisfaction with the service. Therefore, based on the analyses of public sentiment towards the RPSD system and the log files of operation, this study evaluates public satisfaction with RPSD during its trial phase at Munyang Station in the Daegu Subway System.

• Journal of Digital Convergence
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• v.18 no.9
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• pp.17-27
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• 2020

News articles and blog posts have influence on social agenda setting and this study applied text mining on the subject of solar PV in rural area appeared in those media. Texts are gained from online news articles and blog posts with rural solar PV as a keyword by web scrapping, and these are analysed by sentiment analysis and topic modeling technique. Sentiment analysis shows that the proportion of negative texts are significantly lower in blog posts compared to news articles. Result of topic modeling shows that topics related to government policy have the largest loading in positive articles whereas various topics are relatively evenly distributed in negative articles. For blog posts, topics related to rural area installation and environmental damage are have the largest loading in positive and negative texts, respectively. This research reveals issues related to rural solar PV by combining sentiment analysis and topic modeling that were separately applied in previous studies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.160-161
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• 2017

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language Opinion mining and sentiment analysis(OMSA) as a research discipline has emerged during last 15 years and provides a methodology to computationally process the unstructured data mainly to extract opinions and identify their sentiments. The relatively new but fast growing research discipline has changed a lot during these years. This paper presents a scientometric analysis of research work done on OMSA during 2007-2016. For the literature analysis, research publications indexed in Web of Science (WoS) database are used as input data. The publication data is analyzed computationally to identify year-wise publication pattern, rate of growth of publications, research areas. More detailed manual analysis of the data is also performed to identify popular approaches (machine learning and lexcon-based) used in these publications, levels (documents, sentences or aspect-level) of sentiment analysis work done and major application areass of OMSA.