• Smart Media Journal
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• v.10 no.1
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• pp.16-24
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• 2021

The exiting word embedding methodology such as word2vec represents words, which only occur in the raw training corpus, as a fixed-length vector into a continuous vector space, so when mapping the words incorporated in the raw training corpus into a fixed-length vector in morphologically rich language, out-of-vocabulary (OOV) problem often happens. Even for sentence embedding, when representing the meaning of a sentence as a fixed-length vector by synthesizing word vectors constituting a sentence, OOV words make it challenging to meaningfully represent a sentence into a fixed-length vector. In particular, since the agglutinative language, the Korean has a morphological characteristic to integrate lexical morpheme and grammatical morpheme, handling OOV words is an important factor in improving performance. In this paper, we propose parallel Tri-LSTM sentence embedding that is robust to the OOV problem by extending utilizing the morphological information of words into sentence-level. As a result of the sentiment analysis task with corpus in Korean, we empirically found that the character unit is better than the morpheme unit as an embedding unit for Korean sentence embedding. We achieved 86.17% accuracy on the sentiment analysis task with the parallel bidirectional Tri-LSTM sentence encoder.

• Journal of the Korean Wood Science and Technology
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• v.32 no.5
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• pp.12-19
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• 2004

This study was performed to investigate the optimum condition of protease production from submerged culture of oak mushroom (Lentinula edodes, Sanlim No. 5) and its enzymatic features. Among several combinations of media, the combination of wheat bran, corn flour, water and corn oil (WB+CF+W+ CO) yielded 84.8 U/g of maximum protease activity. This combination of ingredients, in spite of not being particularly protein-rich in comparison to the other media, allowed for good growth of the fungus and maximal protease production. Comparison of different growth medium liquids indicated that demineralized water afforded the best growth of the fungus and the highest protease activity. Acetate buffer and acidified water negatively affected The protease production peaked around 72 hr of incubation, and decreased thereafter. The molecular weights of produced protease were about 45,000 by Sephadex G-75 chromatography. The pH optimum for protease activity was 4, while maximal activity incubated at 37℃ for 1 hr was observed between pH 4~6. The optimum temperature of this protease was 55℃, and the enzyme was active over a broad temperature range (30~60℃), indicating that this protease would be suitable for a wide range of applications where. different pH and temperature are necessary, such as digestive aids, food industry, beer and tannery industries.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.19-26
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• 2023

In this study, the public sensitivity of the 'MZ generation' was examined through the social media big data sensitivity analysis method. For the analysis, the consumer account SNS text was examined, and positive and negative emotional factors were presented by classifying external sensibilities and emotions of the MZ generation. In conclusion, the positive emotions of liking and interest in relation to the "MZ generation" were 72.1%, higher than the negative emotional ratio of 27.9%. In positive sensitivity, the older generation showed 'a favorable feeling for the individuality and dignifiedness of the MZ generation' and 'interest in the MZ generation with new values'. In contrast, the MZ generation has a favorable feeling for 'the fact that they are a generation of their own boldness, youthfulness and individuality' and 'small growthism'. Negative sensitivity outside the MZ generation was found to be 'A concern about the marriage avoidance, employment difficulties, debt investment, and resignation trends of the MZ generation', 'Hate the MZ generation who treats Kkondae' and 'Difficult to talk to the MZ generation'. On the other hand, the negative emotions felt by the MZ generation itself were 'Rejection of generalization', 'Rejection of generation and gender conflicts', 'Rejection of competition worse than the older generation', 'Relative failure of the rich era', and 'Sadness to live in a predicted climate disaster'. Therefore, the older generation should not look at the MZ generation in general, but as individuals, and should alleviate conflicts with intergenerational understanding and empathy. there is a need for community consideration to solve generational conflicts, gender conflicts, and environmental problems.

• Smart Media Journal
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• v.12 no.1
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• pp.102-111
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• 2023

As one of the image symbols, the second frame has rich symbolic metaphor. In previous studies, second frames are mostly presented in physical forms such as doors, windows, but in <The Power of the Dog>, there are various forms of second frames, providing more types for the study of second frames. Panofsky's Iconology has put forward a rigorous research method on how to interpret the meaning of image symbols in the picture. This study aims to use Panofsky's Iconology to analyze the second frame in <the Power of the dog>. The purpose is to expand the methodology of film image research and break through the problem that the Iconology analysis of film image stays in narrative analysis (iconographical analysis). It can be seen from the results of this study that the second frame has different visual presentation according to the requirements of narrative. In the narrative of the film, it symbolizes the depressed tone of the film and the stressful relationship between different characters. What director Campion wants to show through the second frame is that in the film industry where the problem of women is getting better, the motif of feminist film creation has changed from the expression of female appeals in binary opposition to the expression of the appeals of diverse groups in "decentralization."

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.67-101
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• 2014

The use of new technology greatly shapes the marketing strategies used by companies to engage their consumers. Among these new technologies, social media is used to reach out to the organization's audience online. One of the most popular social media channels to date is the microblogging platform Twitter. With 500 million tweets sent on average daily, the microblogging platform is definitely a rich source of data for researchers, and a lucrative marketing medium for companies. Nonetheless, one of the challenges for companies in developing an effective Twitter campaign is the limited theoretical and empirical evidence on the proper organizational usage of Twitter despite its potential advantages for a firm's external communications. The current study aims to provide empirical evidence on how firms can utilize Twitter effectively in their marketing communications using the association between brand personality and brand engagement that several branding researchers propose. The study extends Aaker's previous empirical work on brand personality by applying the Brand Personality Scale to explore whether Twitter brand communities convey distinctive brand personalities online and its influence on the communities' level or intensity of consumer engagement and sentiment quality. Moreover, the moderating effect of the product involvement construct in consumer engagement is also measured. By collecting data for a period of eight weeks using the publicly available Twitter application programming interface (API) from 23 accounts of Twitter-verified business-to-consumer (B2C) brands, we analyze the validity of the paper's hypothesis by using computerized content analysis and opinion mining. The study is the first to compare Twitter marketing across organizations using the brand personality concept. It demonstrates a potential basis for Twitter strategies and discusses the benefits of these strategies, thus providing a framework of analysis for Twitter practice and strategic direction for companies developing their use of Twitter to communicate with their followers on this social media platform. This study has four specific research objectives. The first objective is to examine the applicability of brand personality dimensions used in marketing research to online brand communities on Twitter. The second is to establish a connection between the congruence of offline and online brand personalities in building a successful social media brand community. Third, we test the moderating effect of product involvement in the effect of brand personality on brand community engagement. Lastly, we investigate the sentiment quality of consumer messages to the firms that succeed in communicating their brands' personalities on Twitter.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• 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.

• The Korean Journal of Pharmacology
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• v.24 no.1
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• pp.71-81
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• 1988

Glutamate and aspartate may evoke an increase in membrane permeability to monovalent cations and $Ca^{++}$. However, it is uncertain whether $Ca^{++}$ influx is mediated by voltage dependent $Ca^{++}$ channels or by excitatory amino acid activated channels. In addition, the influences of excitatory amino acids on $Ca^{++}$ uptake by neuronal tissues as well as the responses of their actions to extracellular $Mg^{++}$ concentration are different. $K^{+}$ induced $Ca^{++}$ uptake by synaptosomes was dependent on extracellular $Mg^{++}$ up to 5 mM and at concentration of 10 mM, $Ca^{++}$ influx was rather reduced. In $Na^{+}$ rich media, glutamate-and aspartate-induced $Ca^{++}$ uptake was increased by $Mg^{++}$ in a dose independent manner. However, the response for NMDA was inhibited by $Mg^{++}$ at concentrations above 2 mM. $K^+$-and glutamate-induced $Ca^{++}$ influx s were inhibited by 2,4-dinitrophenol, chlorprom-azine and verapamil but not by tetraethylammonium chloride. Tetrodotoxin effectively inhibited the action of glutamate but did not affect that of $K^+$. The response for MNDA was inhibited by 2, 4-dinitrophenol and tetrodotoxin, slightly inhibited by verapamil, and not affected by tetraethylammonium chloride. In $Na^{++}$ rich medium, depolarizing action of glutamate, aspartate and MNDA on synaptosomes was not demonstrated, whereas these agents stimulated $Ca^{++}$ uptake and caused $Ca^{++}$ influx induced depolarization at mitochondria. On the other hand, the activities of synaptosomal ATPases were not affected by excitatory amino acids at 5 mM. The results suggest that glutamate or NMDA induced $Ca^{++}$ influx at synaptosomes exhibits different responses for extracellular $Mg^{++}$ Ex citatory amino acids induced $Ca^{++}$ influx at synaptosomes may be associated with increased permeability of membrane for $Na^{++}$ and $Ca^{++}$ except $K^{++}$ and membrane depolarization due to increased ionic permeability.

• Journal of Microbiology and Biotechnology
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• v.21 no.6
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• pp.637-645
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• 2011

Production of recombinant proteins by excretory expression has many advantages over intracellular expression in Escherichia coli. Hyperexpression of a secretory exoglucanase, Exg, of Cellulomonas fimi was previously shown to saturate the SecYEG pathway and result in dramatic cell death of E. coli. In this study, we demonstrated that overexpression of the PspA in the JM101(pM1VegGcexL-pspA) strain enhanced excretion of Exg to 1.65 U/ml using shake-flask cultivation, which was 80% higher than the highest yield previously obtained from the optimized JM101(pM1VegGcexL) strain. A much higher excreted Exg activity of 4.5 U/ml was further achieved with high cell density cultivation using rich media. Furthermore, we showed that the PspA overexpression strain enjoyed an elevated critical value (CV), which was defined as the largest quotient between the intracellular unprocessed precursor and its secreted mature counterpart that was still tolerable by the host cells prior to the onset of cell death, improving from the previously determined CV of 20/80 to the currently achieved CV of 45/55 for Exg. The results suggested that the PspA overexpression strain might tolerate a higher level of precursor Exg making use of the SecYEG pathway for secretion. The reduced lethal effect might be attributable to the overexpressed PspA, which was postulated to be able to reduce membrane depolarization and damage. Our findings introduce a novel strategy of the combined application of metabolic engineering and construct optimization to the attainment of the best possible E. coli producers for secretory/excretory production of recombinant proteins, using Exg as the model protein.

• The Korean Journal of Pesticide Science
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• v.17 no.4
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• pp.371-378
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• 2013

Various microorganisms live in soil, of which those colonizing rhizosphere interact with nearby plants and tend to develop unique microbial communities. In this study, we isolated diverse actinomycetes from rhizosphere of rice (Oryza sativa L.) cultivated in fertilized (APK) and non-fertilized (NF) paddy soils, and investigated the diversity and antimicrobial activity of them. Using four kinds of selective media, 152 isolates were obtained from the soil samples and identified by determining 16S rRNA gene sequence. All of the isolates showed 99.0%~100.0% similarities with type strains and were classified into six genera: Dactylosporangium, Micromonospora, Kitasatospora, Promicromonospora, Streptomyces and Streptosporangium. Most of the isolates, 143 isolates, were classified into the genus Streptomyces. Additionally, many isolates had antimicrobial activity against plant pathogens, especially Magnaporthe oryzae (rice blast pathogen) in fungi. These findings demonstrated that rice rhizosphere can be a rich source of antagonistic actinomycetes producing diverse bioactive compounds.