• Journal of Environmental Science International
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• v.31 no.7
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• pp.543-553
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• 2022

The latest aerospace technology is important for the stable flight of a launch vehicle, but weather conditions on the day of launch are also one of the essential factors for successful launch campaign. If a launch vehicle is directly struck while preparing to take off from the launch pad on the day of launch or the electronic device are damaged by induced current during flight of the launch vehicle, this means launch failure and can lead to enormous national loss. Therefore, for a successful launch campaign, it is necessary to analyze the lightning detection characteristics of the Naro Space Center. In this study, the seasonal factors of the lightning that occurred over the Naro Space Center from 2003 to 2017, the influence of the polarity, and the correlation with the lightning intensity was confirmed. As a result, there was a high probability of intensive occurrence of multiple lightning strikes in summer, and a high proportion of positive (+) lightning strikes in winter. Lastly, in the distribution of the number of lightning strikes, an average of 2.0 to 2.5 negative (-) lightning strikes occurs in the coastal regions of the South and West Seas when one flash happens.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.315-326
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• 2013

Users in social media post various types of opinions such as product reviews and movie reviews. It is a common trend that customers get assistance from the opinions in making their decisions. However, as opinion usage grows, distorted feedbacks also have increased. For example, exaggerated positive opinions are posted for promoting target products. So are negative opinions which are far from common evaluations. Finding these biased opinions becomes important to keep social media reliable. Techniques of opinion mining (or sentiment analysis) have been developed to determine sentiment polarity of opinionated documents. These techniques can be utilized for finding the biased opinions. However, the previous techniques have some drawback. They categorize the text into only positive and negative, and they also need a large amount of training data to build the classifier. In this paper, we propose methods for discovering the biased opinions which are skewed from the overall common opinions. The methods are based on angle based outlier detection and personalized PageRank, which can be applied without training data. We analyze the performance of the proposed techniques by presenting experimental results on a movie review dataset.

• 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 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.

• Journal of the Korean Chemical Society
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• v.24 no.1
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• pp.44-52
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• 1980

Ionic dissociation rates of $\alpha$-chlorobenzyl ethyl ether in each solvent of toluene-$d_8$ and carbon tetrachloride were measured by the method of dynamic NMR spectroscopy. The spin system of these 1H NMR spectra was $AB_3$. The theoretical spectrum was calculated by computer simulation of dynamic NMR spectra, which agreed very well with observed spectra. From this computer simulation, the ionic dissociation rate constant k was obtained, and by Eyring plot with it, slope and intercept length was gained, from which kinetic parameters were calculated.The easiness of ionic dissociation depended upon solvent polarity. Activation enthalpy was 4.7 kcal/mole in toluene-$d_8$, 10.7 kcal/mole in carbon tetrachloride, and activation entropy was -35. 8 e.u. in toluene-$d_8$, -14.4 e.u. in carbon tetrachloride. It was understood that though the ${\Delta}H^{neq}$ value was small, this ionic dissociation had an easier procession in nonpolar solvents with increasing temperatures. Considering that the ionic dissociation could be thought as the first step of $S_N1$ mechanism, attention might be paid to the results that the value of ${\Delta}S^{neq}$ had a large negative value in comparison with a small ${\Delta}H^{neq}$.

• 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 of Occupational and Environmental Hygiene
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• v.7 no.2
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• pp.209-222
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• 1997

Authors surveyed the ground water near the waste disposed from a fiberglass production factory to confirm the presence of glassfiber in the water and to determine the effect of sampling conditions and storage on the recovery of fibrous materials in the ground water. Sample was collected at every 4 hours for 48 hours consecutively. After finishing the 48 hours sample, water sampling was done from each tap after repeated turning on and off the water for 30 seconds at each time. Sample was collected in the two 1.5 liter polyethylene bottle after vigorously shaking the bottle with the same water several times with the flowing tap water. At each paired sample, one bottle was stored stand still at room temperature, and the other sample was filtered immediately after sampling. Water was filtered on the Mixed Cellulose Ester filter with negative pressure. Each sample was divided into upper and lower layer. The other bottle was stored at room temperature standstill for 7 days and filtered in the same fashion as the other pair of sample did. Each MCE filter was divided into 4 pieces and one piece was treated with acetone to make it transparent. Each prepared sample was observed by two researchers under the light and polarizing microscopy, scanning electron microscopy and energy dispersive X-ra microanalysis. Fibers were classified by the morphology and polarizing pattern under the polarizing microscope, and count was done. 1. There was a significant fluctuation in number of the fibers, but there was no specific demonstrable pattern. 2. Non-polarizing fibers frequently disappeared after 7 days's storage. But cluster of fibers were found at the wall of the same container by scratching technique. 3. Polarizing fibers were usually found in between the filter and the manicure pasted area. Possible explanations for this phenomenon will be that either these fibers are very light or have electronic polarity. Hence, these fibers are not able to be attached on the surface of slide glass. 4. Under the scanning electron microscopic examination, the fibers which are not refractive under the light microscopy were identified as glassfiber. Other fibers which is refractive under the polarizing microscopy were identified as magnesium silicate fibers. It is strongly suggested that development of standardized method of sample collection and measurement of fibrous material in the water is needed.