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Enhancing the Prediction of User Satisfaction with Metaverse Service Through Machine Learning

Computers, Materials & Continua 2022年第9期72卷 4983-4997页

作者： Seon Hong lee haein lee Jang Hyun Kim Department of Applied Artificial Intelligence/Department of Human-Artificial Intelligence Interaction Sungkyunkwan UniversitySeoul03063Korea Department of Interaction Science/Department of Human-Artificial Intelligence Interaction Sungkyunkwan UniversitySeoul03063Korea

Metaverse is one of the main technologies in the daily lives of several people,such as education,tour systems,and mobile application ***,the number of users of mobile metaverse applications is increasing owing to the merit of accessibility *** provide an improved service,it is important to analyze online reviews that contain user *** previous studies have utilized traditional methods,such as the structural equation model(SEM)and technology acceptance method(TAM)for exploring user satisfaction,using limited survey *** methods may not be appropriate for analyzing the users of mobile *** overcome this limitation,several researchers perform user experience analysis through online reviews and star ***,some online reviews occasionally have inconsistencies between the star rating and the sentiment of the *** variation disturbs the performance of machine *** alleviate the inconsistencies,Valence Aware Dictionary and sEntiment Reasoner(VADER),which is a sentiment classifier based on lexicon,is *** current study aims to build a more accurate sentiment classifier based on machine learning with *** this study,five sentiment classifiers are used,such as Naïve Bayes,K-Nearest Neighbors(KNN),Logistic Regression,Light Gradient Boosting Machine(LightGBM),and Categorical boosting algorithm(Catboost)with three embedding methods(Bag-of-Words(BoW),Term Frequency-Inverse Document Frequency(TF-IDF),Word2Vec).The results show that classifiers that apply VADER outperform those that do not apply VADER,excluding one classifier(Logistic Regression with Word2Vec).Moreover,LightGBM with TF-IDF has the highest accuracy 88.68%among other models.

关键词： Metaverse ubiquitous computing user satisfaction online review big data VADER machine learning natural language processing

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ESG Discourse Analysis Through BERTopic: Comparing News Articles and Academic Papers

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Computers, Materials & Continua 2023年第6期75卷 6023-6037页

作者： haein lee Seon Hong lee Kyeo Re lee Jang Hyun Kim Department of Applied Artificial Intelligence/Department of Human-Artificial Intelligence Interaction Sungkyunkwan UniversitySeoul03063Korea Center for SW Education Hanyang UniversityAnsan15588Korea Department of Interaction Science/Department of Human-Artificial Intelligence Interaction Sungkyunkwan UniversitySeoul03063Korea

Environmental,social,and governance(ESG)factors are critical in achieving sustainability in business management and are used as values aiming to enhance corporate ***,non-financial indicators have been considered as important for the actual valuation of corporations,thus analyzing natural language data related to ESG is *** previous studies limited their focus to specific countries or have not used big *** methodologies are insufficient for obtaining potential insights into the best practices to leverage *** address this problem,in this study,the authors used data from two platforms:LexisNexis,a platform that provides media monitoring,and Web of Science,a platform that provides scientific *** big data were analyzed by topic *** modeling can derive hidden semantic structures within the *** this process,it is possible to collect information on public and academic *** authors explored data from a text-mining perspective using bidirectional encoder representations from transformers topic(BERTopic)—a state-of-the-art topic-modeling *** addition,changes in subject patterns over time were considered using dynamic topic *** a result,concepts proposed in an international organization such as the United Nations(UN)have been discussed in academia,and the media have formed a variety of agendas.

关键词： ESG BERTopic natural language processing topic modeling

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Predicting Bitcoin Trends Through Machine Learning Using Sentiment Analysis with Technical Indicators

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Computer Systems Science & Engineering 2023年第8期46卷 2231-2246页

作者： Hae Sun Jung Seon Hong lee haein lee Jang Hyun Kim Department of Applied Artificial Intelligence/Department of Human-Artificial Intelligence Interaction Sungkyunkwan UniversitySeoul03063Korea Department of Interaction Science/Department of Human-Artificial Intelligence Interaction Sungkyunkwan UniversitySeoul03063Korea

Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency *** the history of the Bitcoin market is short and price volatility is high,studies have been conducted on the factors affecting changes in Bitcoin *** have been conducted to predict Bitcoin prices using Twitter ***,the amount of data was limited,and prices were predicted for only a short period(less than two years).In this study,data from Reddit and LexisNexis,covering a period of more than four years,were *** data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of *** accuracy of 90.57%and an area under the receiver operating characteristic curve value(AUC)of 97.48%were obtained using the extreme gradient boosting(XGBoost).It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner(VADER)and 11 technical indicators utilizing moving average,relative strength index(RSI),stochastic oscillators in predicting Bitcoin price trends can produce significant ***,the input features used in the paper can be applied on Bitcoin price ***,this approach allows investors to make better decisions regarding Bitcoin-related investments.

关键词： Bitcoin cryptocurrency sentiment analysis price trends prediction natural language processing machine learning

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Compositional dependence of thermal and elastic properties of Cu-Ti-Zr-Ni bulk metallic glasses

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International Journal of Minerals,Metallurgy and Materials 2010年第3期17卷 318-322页

作者： Jihye An Hyunjune Yim Choi-Yim haein Department of Physics Sookmyung Women's University Department of Mechanical & System Design Engineering Hongik University

Starting from the quaternary Cu47Ti34Zr11Ni8 alloy, the compositional dependence of thermal and elastic properties of Cu-Ti-Zr-Ni alloys was systematically investigated. Quaternary Cu-Ti-Zr-Ni alloys can be cast directly from the melt into copper molds to form fully amorphous strips or rods with the thickness of 3-6 mm. The evidence of the amorphous nature of the cast rods was provided by X-ray spectra. The measured glass transition temperature （Tg） and crystallization temperature （Tx） were obtained for the alloys using differential scanning calorimetry （DSC） at the heating rate of 20 K/s. In the results, the differences between the glass temperature and the crystallization temperature （△Tx=Tx-Tg） are measured with values ranging up to 33-55 K. The reduced glass transition temperature （Trg）, which is the ratio of the glass temperature to the liquidus temperature （T3, is often used as an indication of the glass-forming ability of metallic alloys. For the present Cu-Ti-Zr-Ni alloys, this ratio is typically in the range of 0.5838-0.5959, characteristic of metallic alloys with good glass-forming ability. The elastic constants for several selected alloys were measured using ultrasonic methods. The values of the elastic shear modulus, bulk modulus, and Poisson＇s ratio were also given.

关键词： bulk metallic glasses (BMGs) quatemary alloys elastic constants glass-forming ability

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Co含量对磁性非晶合金Fe85-xCoxTi7Hf6B2电学特性的影响

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稀有金属 2019年第7期43卷 693-698页

作者：陈澄黄琳 Kim Sumin 朴红光 haein Choi-Yim Kim Dong-Hyun 三峡大学理学院&磁电子与纳磁探测研究所湖北宜昌443002 忠北大学物理系韩国清州28644 淑明女子大学物理系韩国首尔04310

研究了由单铜辊真空熔融纺丝技术制备的扁平带状铁基磁性非晶态合金Fe(85-x)CoxTi7Hf6B2(x=20, 30, 35,原子分数)的电学特性。这种软铁磁性非晶合金与其他含FeCo类合金一样,当Co含量达到30%附近时其饱和磁化强度会呈现最大值。尽管如此... 详细信息

研究了由单铜辊真空熔融纺丝技术制备的扁平带状铁基磁性非晶态合金Fe(85-x)CoxTi7Hf6B2(x=20, 30, 35,原子分数)的电学特性。这种软铁磁性非晶合金与其他含FeCo类合金一样,当Co含量达到30%附近时其饱和磁化强度会呈现最大值。尽管如此,体系中Co含量的改变并没有对这种铁基非晶合金的磁性能产生较大的影响。值得注意的是,在这种铁基非晶合金体系中Co含量的改变可导致其电学特性发生显著的转变。比如:当Co含量为20%时,其电阻温度系数为负值,表现出类似于半导体的电阻温度特性和P型半导体的电学特性;然而,当Co含量增加到30%和35%时,其电阻温度系数则转变为正值,呈现出类似一般金属导体的电阻温度特性。不仅如此,根据该非晶合金中各组成元素的电负性和电离势揭示了Co含量可调控载流子(空穴)浓度的物理机制,为新型铁基非晶合金材料的设计提供新方法。

关键词：非晶态合金软铁磁性能磁性半导体电阻温度系数

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