• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.687-692
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• 2023

We analyze the accuracy of emotion analysis of natural language processing deep learning model and propose to use it for emotional content development. After looking at the outline of the GPT-3 model, about 6,000 pieces of dialogue data provided by Aihub were input to 9 emotion categories: 'joy', 'sadness', 'fear', 'anger', 'disgust', and 'surprise'. ', 'interest', 'boredom', and 'pain'. Performance evaluation was conducted using the evaluation indices of accuracy, precision, recall, and F1-score, which are evaluation methods for natural language processing models. As a result of the emotion analysis, the accuracy was over 91%, and in the case of precision, 'fear' and 'pain' showed low values. In the case of reproducibility, a low value was shown in negative emotions, and in the case of 'disgust' in particular, an error appeared due to the lack of data. In the case of previous studies, emotion analysis was mainly used only for polarity analysis divided into positive, negative, and neutral, and there was a limitation in that it was used only in the feedback stage due to its nature. We expand emotion analysis into 9 categories and suggest its use in the development of emotional content considering it from the planning stage. It is expected that more accurate results can be obtained if emotion analysis is performed by additionally collecting more diverse daily conversations through follow-up research.

• Journal of Korean Society of Environmental Engineers
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• v.35 no.12
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• pp.897-905
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• 2013

In this study, sorption coefficients (${\log}K_{OC}$, n) for the binding of phenanthrene (PHE) to soil humins, insoluble fraction of soil humc substances (HS), were determined and relationship between the sorption coefficients and structural characteristics of the soil humins were investigated. The soil humins used in the present study were isolated from 7 different soils including 5 domestic soils, an IHSS standard and a peat soil, and characterized by elemental analysis and CPMAS $^{13}C$ NMR method. $^{13}C$ NMR spectral features indicate that the soil humins are mainly made up of aliphatic carbons (57.1~72.3% in total carbon) with high alkyl-C moiety, and the alkyl-C contents ($C_{Al-H,C}$, %) was in order of granite soil Hu (26~42%) > volcanic ash soil, HL Hu (23.9%) > Peat Hu (14.0%). The results of correlation study show that a positive relationship ($r^2$ = 0.77, p < 0.05) between organic carbon normalized-sorption coefficients ($K_{OC}$, mL/g) and alkyl-C contents($C_{Al-H,C}$, %), while negative relationship ($r^2$ = (-)0.74, p < 0.05) between Freundlich sorption parameter (n) and H,C-substituted aromatic carbon contents ($C_{Ar-H,C}$, %). The magnitude of $K_{OC}$ values are also negatively well correlated with polarity index (e.g., PI, N + O)/C) ($r^2$ = (-)0.74, p < 0.1). These results suggest that the binding capacity (e.g., $K_{OC}$) for PHE is increased in soil humin molecules having high contents of alkyl-C or lower polarity, and nonlinear sorption for PHE increased as the H,C-substituted aromatic carbon contents ($C_{Ar-H,C}$, %) in the soil humins increased. The PHE sorption characteristics on soil humins are discussed based on the dual reactive mode of sorption model.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.75-94
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• 2021

The low birth rate and shortened military service period are causing concerns about selecting excellent military officers. The Republic of Korea entered a low birth rate society in 1984 and an aged society in 2018 respectively, and is expected to be in a super-aged society in 2025. In addition, the troop-oriented military is changed as a state-of-the-art weapons-oriented military, and the reduction of the military service period was implemented in 2018 to ease the burden of military service for young people and play a role in the society early. Some observe that the application rate for military officers is falling due to a decrease of manpower resources and a preference for shortened mandatory military service over military officers. This requires further consideration of the policy of securing excellent military officers. Most of the related studies have used social scientists' methodologies, but this study applies the methodology of text mining suitable for large-scale documents analysis. This study extracts words of discriminative characteristics from the Republic of Korea Air Force Non-Commissioned Officer Applicant cover letters and analyzes the polarity of pass and fail. It consists of three steps in total. First, the application is divided into general and technical fields, and the words characterized in the cover letter are ordered according to the difference in the frequency ratio of each field. The greater the difference in the proportion of each application field, the field character is defined as 'more discriminative'. Based on this, we extract the top 50 words representing discriminative characteristics in general fields and the top 50 words representing discriminative characteristics in technology fields. Second, the number of appropriate topics in the overall cover letter is calculated through the LDA. It uses perplexity score and coherence score. Based on the appropriate number of topics, we then use LDA to generate topic and probability, and estimate which topic words of discriminative characteristic belong to. Subsequently, the keyword indicators of questions used to set the labeling candidate index, and the most appropriate index indicator is set as the label for the topic when considering the topic-specific word distribution. Third, using L-LDA, which sets the cover letter and label as pass and fail, we generate topics and probabilities for each field of pass and fail labels. Furthermore, we extract only words of discriminative characteristics that give labeled topics among generated topics and probabilities by pass and fail labels. Next, we extract the difference between the probability on the pass label and the probability on the fail label by word of the labeled discriminative characteristic. A positive figure can be seen as having the polarity of pass, and a negative figure can be seen as having the polarity of fail. This study is the first research to reflect the characteristics of cover letters of Republic of Korea Air Force non-commissioned officer applicants, not in the private sector. Moreover, these methodologies can apply text mining techniques for multiple documents, rather survey or interview methods, to reduce analysis time and increase reliability for the entire population. For this reason, the methodology proposed in the study is also applicable to other forms of multiple documents in the field of military personnel. This study shows that L-LDA is more suitable than LDA to extract discriminative characteristics of Republic of Korea Air Force Noncommissioned cover letters. Furthermore, this study proposes a methodology that uses a combination of LDA and L-LDA. Therefore, through the analysis of the results of the acquisition of non-commissioned Republic of Korea Air Force officers, we would like to provide information available for acquisition and promotional policies and propose a methodology available for research in the field of military manpower acquisition.

• Biomedical Science Letters
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• v.13 no.1
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• pp.25-32
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• 2007

The purpose of this study was to investigate the effect of the high voltage pulsed Current (HVPC) stimulation on the healing rate and the proliferative activity of keratinocytes and IGF-I mRNA expression of an incisional wound in rat skin. Twenty male Sprague-Dawley rats ($265{\sim}290g$) were randomly divided into HVPC (n=10) and control group (n=10). Rats received 10 mm length of full-thickness incision wound on the back under the anesthesia. The HVPC group received electrical stimulation with a Current intensity of 50 V at 100 pps for a duration of 30 minutes, while the control group was given the same treatment without electricity for a week. Polarity was negative in first three days and positive thereafter. The wound length was measured and evaluated as percentage. The mean number of nucleolar organizer regions (NORs) per nucleus and level of IGF-I mRNA expression were calculated. The mean percent of wound closure were $51.17{\pm}17.76%$ and $80.71{\pm}11.91%$, respectively, in the sham treated control and HVPC stimulated groups (t=-4.308, P<0.001). The mean NOR number per nucleus of the keratinocytes in the control and HVPC group were $1.85{\pm}0.20$ and $2.70{\pm}0.23$, respectively (t=8.638, P<0.001). The IGF-I mRNA level were $0.76{\pm}0.44$ and $1.32{\pm}0.41$, respectively, in the control and HVPC stimulated wounds (t=2.906, P<0.01). There was a positive correlation between the mean NOR number per nucleus and IGF-l mRNA level with a Pearson product moment correlation coefficient of 0.72 (P<0.05). These findings suggest that the HVPC may activate the rRNA of the basal keratinocytes and upregulate the IGF-I mRNA levels by alteration of the electrical environment, and it may increase proliferative activity of the keratinocytes in the skin wound of the rat.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.49-67
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• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.

• 한국해양학회지
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• v.26 no.1
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• pp.1-12
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• 1991

A multi-disciplinary geophysical study including gravity, magnetic, and seismic reflection profiling was carried out in the area between the Clarion fracture zone and the Clippertone fracture zone o the northeastern equatorial Pacific basin. There are small free-air gravity anomalies of less than 20 mgal over seamounts and the east-west trending abyssal hills. The negative residual gravity anomalies over seamounts may indicate the existence of low density seamount roots compared to surrounding oceanic crust. Non-existence of magnetic lineations and the magnetic anomalies of small smplitude with no polarity change in the east-west direction support that the study area belongs to the Cretaceous magnetic quite zone. Positive magnetic anomalies over seamounts offset 100 km in the east-west direction in the southern part of the study area suggest a possibility of left-lateral movement of those seamounts along unknown fractures. The sedimentary section in the study area can be divided into three units (Unit I, unit IIA, and Unit IIB) n the basis of reflection characteristics. the total thickness of sedimentary section varies from 200 to 400 meters and the sedimentary section is thicker in the southern area of rough topography near the seamount belt than in the northern flat area. Manganese nodules are abundant in the southern part of the study area where the ridges are developed and the Unit I layer is thicker than 100 meters.

• Journal of Powder Materials
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• v.22 no.5
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• pp.321-325
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• 2015

The triboelectric property of a material is important to improve an efficiency of triboelectric generator (TEG) in energy harvesting from an ambient energy. In this study, we have studied the TEG property of a semiconducting $SnO_2$ which has yet to be explored so far. As a counter triboelectric material, PET and glass are used. Vertical contact mode is utilized to evaluate the TEG efficiency. $SnO_2$ thin film is deposited by atomic layer deposition on bare Si wafer for various thicknesses from 5.2 nm to 34.6 nm, where the TEG output is increased from 13.9V to 73.5V. Triboelectric series are determined by comparing the polarity of output voltage of 2 samples among $SnO_2$, PET, and glass. In conclusion, $SnO_2$, as an intrinsic n-type material, has the most strong tendency to be positive side to lose the electron and PET has the most strong tendency to be negative side to get the electron, and glass to be between them. Therefore, the $SnO_2$-PET combination shows the highest TEG efficiency.

• Korean Journal of Cognitive Science
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• v.21 no.1
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• pp.157-175
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• 2010

Understanding dialogue participant's emotion is important as well as decoding the explicit message in human communication. It is well known that non-verbal elements are more suitable for conveying speaker's emotions than verbal elements. Written texts, however, contain a variety of linguistic units that express emotions. This study aims at analyzing components for constructing an emotion ontology, that provides us with numerous applications in Human Language Technology. A majority of the previous work in text-based emotion processing focused on the classification of emotions, the construction of a dictionary describing emotion, and the retrieval of those lexica in texts through keyword spotting and/or syntactic parsing techniques. The retrieved or computed emotions based on that process did not show good results in terms of accuracy. Thus, more sophisticate components analysis is proposed and the linguistic factors are introduced in this study. (1) 5 linguistic types of emotion expressions are differentiated in terms of target (verbal/non-verbal) and the method (expressive/descriptive/iconic). The correlations among them as well as their correlation with the non-verbal expressive type are also determined. This characteristic is expected to guarantees more adaptability to our ontology in multi-modal environments. (2) As emotion-related components, this study proposes 24 emotion types, the 5-scale intensity (-2~+2), and the 3-scale polarity (positive/negative/neutral) which can describe a variety of emotions in more detail and in standardized way. (3) We introduce verbal expression-related components, such as 'experiencer', 'description target', 'description method' and 'linguistic features', which can classify and tag appropriately verbal expressions of emotions. (4) Adopting the linguistic tag sets proposed by ISO and TEI and providing the mapping table between our classification of emotions and Plutchik's, our ontology can be easily employed for multilingual processing.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.2
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• pp.3-12
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• 2017

Leaving positive or negative comments of places where he or she visits on location-based services is being common in daily life. The sentiment analysis of place reviews written by actual visitors can provide valuable information to potential consumers, as well as business owners. To conduct sentiment analysis of a place, a spatial sentiment lexicon that can be used as a criterion is required; yet, lexicon of spatial sentiment words has not been constructed. Therefore, this study suggested a method to construct a spatial sentiment lexicon by analyzing the place review data written by Korean internet users. Among several location categories, theme parks were chosen for this study. For this purpose, natural language processing technique and statistical techniques are used. Spatial sentiment words included the lexicon have information about sentiment polarity and probability score. The spatial sentiment lexicon constructed in this study consists of 3 tables(SSLex_SS, SSLex_single, SSLex_combi) that include 219 spatial sentiment words. Throughout this study, the sentiment analysis has conducted based on the texts written about the theme parks created on Twitter. As the accuracy of the sentiment classification was calculated as 0.714, the validity of the lexicon was verified.

• The Journal of Society for e-Business Studies
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• v.16 no.4
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• pp.125-137
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• 2011

Product reviews in online shopping sites can serve as a useful guideline to buying decisions of customers. However, due to the massive amount of such reviews, it is almost impossible for users to read all the product reviews. For this reason, e-commerce sites provide users with useful reviews or statistics of ratings on products that are manually chosen or calculated. Opinion mining or sentiment analysis is a study on automating above process that involves firstly analyzing users' reviews on a product to tell if a review contains positive or negative feedback, and secondly, providing a summarized report of users' opinions. Previous researches focus on either providing polarity of a user's opinion or summarizing user's opinion on a feature of a product that result in relatively low usage of information that a user review contains. Actual user reviews contains not only mere assessment of a product, but also dissatisfaction and flaws of a product that a user experiences. There are increasing needs for effective analysis on such criteria to help users on their decision-making process. This paper proposes a model that stores various types of user reviews in a data warehouse, and analyzes integrated reviews dynamically. Also, we analyze reviews of an online application shopping site with the proposed model.