• Title/Summary/Keyword: information expression

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Pathos of Color Green Expressed in Korean War Films (전쟁영화에서 초록의 색채표현과 파토스)

  • Jong-Guk Kim
    • Journal of Information Technology Applications and Management
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    • v.29 no.6
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    • pp.123-134
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    • 2022
  • War films are a general term for films that have battlefields as their main background. Although war films as a genre directly deal with combat situations, they also deal with characters or subjects related to war. War films promote patriotism and nationalism, but they also argue against war by highlighting the disastrous war. This study is based on the color theory that the meaning of film color is temporarily and infinitely generated according to the cultural differences, with Eisenstein's creative theory on film color and pathos. I wanted to clarify the pathos effect and the meaning of color green expressed in the Korean war films. In war films, colors are visualized in art forms such as symbols, similes and metaphors. In war films, color green symbolizes life. On the battlefield, the green of nature stands against the catastrophic situation. The green of ecology, which insists on the flow of life, evokes fear in ecological crises such as war, disaster and climate change. The dark green caused by a catastrophe like war warns of the destruction of life. The connotation of color is temporarily and infinitely expands according to the cultural differences. The dark green, which visualizes the battlefield of destruction, is a form and element of pathos that indicates changes in emotions such as sadness, pity, grief and despair. Pathos as an emotional appeal is a leap from the quality to the quality of the means of expression and refers to the departure from Dasein. The green color that dominates the visuals of war films is a symbol of life and functions as a pathos that makes emotional changes take a new leap. A qualitative leap through pathos means all changes that become new.

Genome-wide identification of histone lysine methyltransferases and their implications in the epigenetic regulation of eggshell formation-related genes in a trematode parasite Clonorchis sinensis

  • Min-Ji Park;Woon-Mok Sohn;Young-An Bae
    • Parasites, Hosts and Diseases
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    • v.62 no.1
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    • pp.98-116
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    • 2024
  • Epigenetic writers including DNA and histone lysine methyltransferases (DNMT and HKMT, respectively) play an initiative role in the differentiation and development of eukaryotic organisms through the spatiotemporal regulation of functional gene expressions. However, the epigenetic mechanisms have long been suspected in helminth parasites lacking the major DNA methyltransferases DNMT1 and DNMT3a/3b. Very little information on the evolutionary status of the epigenetic tools and their role in regulating chromosomal genes is currently available in the parasitic trematodes. We previously suggested the probable role of a DNMT2-like protein (CsDNMT2) as a genuine epigenetic writer in a trematode parasite Clonorchis sinensis. Here, we analyzed the phylogeny of HKMT subfamily members in the liver fluke and other platyhelminth species. The platyhelminth genomes examined conserved genes for the most of SET domain-containing HKMT and Disruptor of Telomeric Silencing 1 subfamilies, while some genes were expanded specifically in certain platyhelminth genomes. Related to the high gene dosages for HKMT activities covering differential but somewhat overlapping substrate specificities, variously methylated histones were recognized throughout the tissues/organs of C. sinensis adults. The temporal expressions of genes involved in eggshell formation were gradually decreased to their lowest levels proportionally to aging, whereas those of some epigenetic tool genes were re-boosted in the later adult stages of the parasite. Furthermore, these expression levels were significantly affected by treatment with DNMT and HKMT inhibitors. Our data strongly suggest that methylated histones are potent epigenetic markers that modulate the spatiotemporal expressions of C. sinensis genes, especially those involved in sexual reproduction.

Bacterial communities in the feces of insectivorous bats in South Korea

  • Injung An;Byeori Kim;Sungbae Joo;Kihyun Kim;Taek-Woo Lee
    • Journal of Ecology and Environment
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    • v.48 no.2
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    • pp.120-127
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    • 2024
  • Bats serve as vectors and natural reservoir hosts for various infectious viruses, bacteria, and fungi. These pathogens have also been detected in bat feces and can cause severe illnesses in hosts, other animals, and humans. Because pathogens can easily spread into the environment through bat feces, determining the bacterial communities in bat guano is crucial to mitigate potential disease transmission and outbreaks. This study primarily aimed to examine bacterial communities in the feces of insectivorous bats living in South Korea. Fecal samples were collected after capturing 84 individuals of four different bat species in two regions of South Korea, and the bacterial microbiota was assessed through next generation sequencing of the 16S rRNA gene. The results revealed that, with respect to the relative abundance at the phylum level, Myotis bombinus was dominated by Firmicutes (47.24%) and Proteobacteria (42.66%) whereas Miniopterus fuliginosus (82.78%), Rhinolophus ferrumequinum (63.46%), and Myotis macrodactylus (78.04%) were dominated by Proteobacteria. Alpha diversity analysis showed no difference in abundance between species and a significant difference (p < 0.05) between M. bombinus and M. fuliginosus. Beta-diversity analysis revealed that Clostridium, Asaia, and Enterobacteriaceae_g were clustered as major factors at the genus level using principal component analysis. Additionally, linear discriminant analysis effect size was conducted based on relative expression information to select bacterial markers for each bat species. Clostridium was relatively abundant in M. bombinus, whereas Mycoplasma_g10 was relatively abundant in R. ferrumequinum. Our results provide an overview of bat guano microbiota diversity and the significance of pathogenic taxa for humans and the environment, highlighting a better understanding of preventing emerging diseases. We anticipate that this research will yield bioinformatic data to advance our knowledge of overall microbial genetic diversity and clustering characteristics in insectivorous bat feces in South Korea.

Development of a nucleic acid detection method based on the CRISPR-Cas13 for point-of-care testing of bovine viral diarrhea virus-1b

  • Sungeun Hwang;Wonhee Lee;Yoonseok Lee
    • Journal of Animal Science and Technology
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    • v.66 no.4
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    • pp.781-791
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    • 2024
  • Bovine viral diarrhea (BVD) is a single-stranded, positive-sense ribonucleic acid (RNA) virus belonging to the genus Pestivirus of the Flaviviridae family. BVD frequently causes economic losses to farmers. Among bovine viral diarrhea virus (BVDV) strains, BVDV-1b is predominant and widespread in Hanwoo calves. Reverse-transcription polymerase chain reaction (RT-PCR) is an essential method for diagnosing BVDV-1b and has become the gold standard for diagnosis in the Republic of Korea. However, this diagnostic method is time-consuming and requires expensive equipment. Therefore, Clustered regularly interspaced short palindromic repeats-Cas (CRISPR-Cas) systems have been used for point-of-care (POC) testing of viruses. Developing a sensitive and specific method for POC testing of BVDV-1b would be advantageous for controlling the spread of infection. Thus, this study aimed to develop a novel nucleic acid detection method using the CRISPR-Cas13 system for POC testing of BVDV-1b. The sequence of the BVD virus was extracted from National Center for Biotechnology Information (NC_001461.1), and the 5' untranslated region, commonly used for detection, was selected. CRISPR RNA (crRNA) was designed using the Cas13 design program and optimized for the expression and purification of the LwCas13a protein. Madin Darby bovine kidney (MDBK) cells were infected with BVDV-1b, incubated, and the viral RNA was extracted. To enable POC viral detection, the compatibility of the CRISPR-Cas13 system was verified with a paper-based strip through collateral cleavage activity. Finally, a colorimetric assay was used to evaluate the detection of BVDV-1b by combining the previously obtained crRNA and Cas13a protein on a paper strip. In conclusion, the CRISPR-Cas13 system is highly sensitive, specific, and capable of nucleic acid detection, making it an optimal system for the early point-of-care testing of BVDV-1b.

Analysis of Domestic Research Trends and Tools of Digital Alternative Learning Materials for Digital Tactile Graphic Production of Blind Students (시각장애학생의 디지털 촉각그래픽 제작을 위한 디지털 대체학습자료의 국내 연구동향 및 도구 분석)

  • Tae-eun Lee;Hee-ju Lee
    • Journal of Practical Engineering Education
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    • v.16 no.5_spc
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    • pp.653-661
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    • 2024
  • This study aims to derive implications for the development of digital tactile graphics by analyzing research trends and product trends of visually impaired students through Naver's academic information over the past 10 years. Research on 'digital data' and 'digital tactile graphics' was mainly conducted, and the 'in-depth analysis' method was used a lot. In the research content, 'research trend analysis' was high, and 'research purpose' was most often performed, and empirical research was insufficient. Domestic tactile graphic products need to be improved because they have fewer expression cells than overseas, and production guides need to be produced through empirical research in the future.

Manganese and Iron Interaction: a Mechanism of Manganese-Induced Parkinsonism

  • Zheng, Wei
    • Proceedings of the Korea Environmental Mutagen Society Conference
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    • 2003.10a
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    • pp.34-63
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    • 2003
  • Occupational and environmental exposure to manganese continue to represent a realistic public health problem in both developed and developing countries. Increased utility of MMT as a replacement for lead in gasoline creates a new source of environmental exposure to manganese. It is, therefore, imperative that further attention be directed at molecular neurotoxicology of manganese. A Need for a more complete understanding of manganese functions both in health and disease, and for a better defined role of manganese in iron metabolism is well substantiated. The in-depth studies in this area should provide novel information on the potential public health risk associated with manganese exposure. It will also explore novel mechanism(s) of manganese-induced neurotoxicity from the angle of Mn-Fe interaction at both systemic and cellular levels. More importantly, the result of these studies will offer clues to the etiology of IPD and its associated abnormal iron and energy metabolism. To achieve these goals, however, a number of outstanding questions remain to be resolved. First, one must understand what species of manganese in the biological matrices plays critical role in the induction of neurotoxicity, Mn(II) or Mn(III)? In our own studies with aconitase, Cpx-I, and Cpx-II, manganese was added to the buffers as the divalent salt, i.e., $MnCl_2$. While it is quite reasonable to suggest that the effect on aconitase and/or Cpx-I activites was associated with the divalent species of manganese, the experimental design does not preclude the possibility that a manganese species of higher oxidation state, such as Mn(III), is required for the induction of these effects. The ionic radius of Mn(III) is 65 ppm, which is similar to the ionic size to Fe(III) (65 ppm at the high spin state) in aconitase (Nieboer and Fletcher, 1996; Sneed et al., 1953). Thus it is plausible that the higher oxidation state of manganese optimally fits into the geometric space of aconitase, serving as the active species in this enzymatic reaction. In the current literature, most of the studies on manganese toxicity have used Mn(II) as $MnCl_2$ rather than Mn(III). The obvious advantage of Mn(II) is its good water solubility, which allows effortless preparation in either in vivo or in vitro investigation, whereas almost all of the Mn(III) salt products on the comparison between two valent manganese species nearly infeasible. Thus a more intimate collaboration with physiochemists to develop a better way to study Mn(III) species in biological matrices is pressingly needed. Second, In spite of the special affinity of manganese for mitochondria and its similar chemical properties to iron, there is a sound reason to postulate that manganese may act as an iron surrogate in certain iron-requiring enzymes. It is, therefore, imperative to design the physiochemical studies to determine whether manganese can indeed exchange with iron in proteins, and to understand how manganese interacts with tertiary structure of proteins. The studies on binding properties (such as affinity constant, dissociation parameter, etc.) of manganese and iron to key enzymes associated with iron and energy regulation would add additional information to our knowledge of Mn-Fe neurotoxicity. Third, manganese exposure, either in vivo or in vitro, promotes cellular overload of iron. It is still unclear, however, how exactly manganese interacts with cellular iron regulatory processes and what is the mechanism underlying this cellular iron overload. As discussed above, the binding of IRP-I to TfR mRNA leads to the expression of TfR, thereby increasing cellular iron uptake. The sequence encoding TfR mRNA, in particular IRE fragments, has been well-documented in literature. It is therefore possible to use molecular technique to elaborate whether manganese cytotoxicity influences the mRNA expression of iron regulatory proteins and how manganese exposure alters the binding activity of IPRs to TfR mRNA. Finally, the current manganese investigation has largely focused on the issues ranging from disposition/toxicity study to the characterization of clinical symptoms. Much less has been done regarding the risk assessment of environmenta/occupational exposure. One of the unsolved, pressing puzzles is the lack of reliable biomarker(s) for manganese-induced neurologic lesions in long-term, low-level exposure situation. Lack of such a diagnostic means renders it impossible to assess the human health risk and long-term social impact associated with potentially elevated manganese in environment. The biochemical interaction between manganese and iron, particularly the ensuing subtle changes of certain relevant proteins, provides the opportunity to identify and develop such a specific biomarker for manganese-induced neuronal damage. By learning the molecular mechanism of cytotoxicity, one will be able to find a better way for prediction and treatment of manganese-initiated neurodegenerative diseases.

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The Effects of Treadmill Exercise on Cognitive Performance, Brain Mitochondrial Aβ-42, Cytochrome c, SOD-1, 2 and Sirt-3 Protein Expression in Mutant (N141I) Presenilin-2 Transgenic Mice of Alzheimer's Disease (트레드밀 운동이 mutant (N141I) presenilin-2 유전자를 이식한 알츠하이머질환 모델 생쥐 뇌의 Aβ-42, cytochrome c, SOD-1, 2와 Sirt-3 단백질 발현에 미치는 영향)

  • Koo, Jung-Hoon;Eum, Hyun-Sub;Kang, Eun-Bum;Kwon, In-Su;Yeom, Dong-Cheol;An, Gil-Young;Oh, Yoo-Sung;Baik, Young-Soo;Cho, In-Ho;Cho, Joon-Yong
    • Journal of Life Science
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    • v.20 no.3
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    • pp.444-452
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    • 2010
  • The purpose of this study was to investigate the effects of treadmill exercise on $A{\beta}$-42, cytochrome c, SOD-1, 2 and Sirt-3 protein expressions in brain cytosol and mitochondria in mutant (N141I) presenilin-2 transgenic mice with Alzheimer's disease (AD). The mice were divided into four groups (Non-Tg-sedentary, n=5; Non-Tg treadmill exercise, n=5; Tg-sedentary, n=5; Tg treadmill exercise, n=5). To evaluate the neuroprotective effect of treadmill exercise, Non-Tg and Tg mice were subjected to exercise training on a treadmill for 12 wk, after which their brain cytosol and mitochondria were evaluated to determine whether any changes in the cognitive performance, $A{\beta}$-42 protein, cytochrome c protein, anti-oxidant enzymes (SOD-1, SOD-2) and Sirt-3 protein had occurred. The results indicated that treadmill exercise resulted in amelioration in cognitive deficits of Tg mice. In addition, the expressions of mitochondrial $A{\beta}$-42 and cytosolic cytochrome c protein were decreased in the brains of Tg mice after treadmill exercise, whereas antioxidant enzymes, SOD-l and SOD-2 were significantly increased in response to treadmill exercise. Furthermore, treadmill exercise significantly increased the expression of Sirt-3 protein in Non-Tg and Tg mice. Taken together, these results suggest that treadmill exercise is a simple behavioral intervention which can sufficiently improve cognitive performance and inhibit $A{\beta}$-induced oxidative stress in AD.

The Intelligent Determination Model of Audience Emotion for Implementing Personalized Exhibition (개인화 전시 서비스 구현을 위한 지능형 관객 감정 판단 모형)

  • Jung, Min-Kyu;Kim, Jae-Kyeong
    • Journal of Intelligence and Information Systems
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    • v.18 no.1
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    • pp.39-57
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    • 2012
  • Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

A Study on Human-Robot Interaction Trends Using BERTopic (BERTopic을 활용한 인간-로봇 상호작용 동향 연구)

  • Jeonghun Kim;Kee-Young Kwahk
    • Journal of Intelligence and Information Systems
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    • v.29 no.3
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    • pp.185-209
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    • 2023
  • With the advent of the 4th industrial revolution, various technologies have received much attention. Technologies related to the 4th industry include the Internet of Things (IoT), big data, artificial intelligence, virtual reality (VR), 3D printers, and robotics, and these technologies are often converged. In particular, the robotics field is combined with technologies such as big data, artificial intelligence, VR, and digital twins. Accordingly, much research using robotics is being conducted, which is applied to distribution, airports, hotels, restaurants, and transportation fields. In the given situation, research on human-robot interaction is attracting attention, but it has not yet reached the level of user satisfaction. However, research on robots capable of perfect communication is steadily being conducted, and it is expected that it will be able to replace human emotional labor. Therefore, it is necessary to discuss whether the current human-robot interaction technology can be applied to business. To this end, this study first examines the trend of human-robot interaction technology. Second, we compare LDA (Latent Dirichlet Allocation) topic modeling and BERTopic topic modeling methods. As a result, we found that the concept of human-robot interaction and basic interaction was discussed in the studies from 1992 to 2002. From 2003 to 2012, many studies on social expression were conducted, and studies related to judgment such as face detection and recognition were conducted. In the studies from 2013 to 2022, service topics such as elderly nursing, education, and autism treatment appeared, and research on social expression continued. However, it seems that it has not yet reached the level that can be applied to business. As a result of comparing LDA (Latent Dirichlet Allocation) topic modeling and the BERTopic topic modeling method, it was confirmed that BERTopic is a superior method to LDA.

Recommender system using BERT sentiment analysis (BERT 기반 감성분석을 이용한 추천시스템)

  • Park, Ho-yeon;Kim, Kyoung-jae
    • Journal of Intelligence and Information Systems
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    • v.27 no.2
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    • pp.1-15
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    • 2021
  • If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.