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Lee, Gichang,Jeong, Jaeyun,Seo, Seungwan,Kim, CzangYeob,Kang, Pilsung Elsevier 2018 Knowledge-based systems Vol.152 No.-
<P><B>Abstract</B></P> <P>In order to maximize the applicability of sentiment analysis results, it is necessary to not only classify the overall sentiment (positive/negative) of a given document but also to identify the main words that contribute to the classification. However, most datasets for sentiment analysis only have the sentiment label for each document or sentence. In other words, there is a lack of information about which words play an important role in sentiment classification. In this paper, we propose a method for identifying key words discriminating positive and negative sentences by using a weakly supervised learning method based on a convolutional neural network (CNN). In our model, each word is represented as a continuous-valued vector and each sentence is represented as a matrix whose rows correspond to the word vector used in the sentence. Then, the CNN model is trained using these sentence matrices as inputs and the sentiment labels as the output. Once the CNN model is trained, we implement the word attention mechanism that identifies high-contributing words to classification results with a class activation map, using the weights from the fully connected layer at the end of the learned CNN model. To verify the proposed methodology, we evaluated the classification accuracy and the rate of polarity words among high scoring words using two movie review datasets. Experimental results show that the proposed model can not only correctly classify the sentence polarity but also successfully identify the corresponding words with high polarity scores.</P>
Hwan-Hee Jang,Hwayoung Noh,Gichang Kim,Soo-Yeon Cho,Hyeon-Jung Kim,Jeong Seon Kim,Pekka Keski-Rahkonen,Heinz Freisling,Marc Gunter,Hyesook Kim,Oran Kwon 한국식품영양과학회 2021 한국식품영양과학회 학술대회발표집 Vol.2021 No.10
A holistic approach to personalized nutrition to maintain health and prevent disease is advancing with data mining technology. Our health could be affected by diet-gut microbiota interactions, together with metabolites derived by the interaction, resulting in personalized responses. This study will examine associations of diet-gut microbiota interactions with human metabolites and health parameters including metabolic health- related markers. In a cross-sectional study of 350 adults aged 19-60 years, we will assess diet using a food frequency questionnaire, gut microbiota in stool using 16s rRNA sequencing, and metabolites will be measured in fasting blood and 12h overnight urine using an untargeted metabolomic approach. We will develop machine-learning algorithms and predict individual risk by selecting core features related to metabolic health by integrating multiple data such as microbiome, metabolome, health parameters, and lifestyle factors. Healthy diets can be estimated by deriving foods that are relevant to personalized predictive scores. Further studies are needed to verify the health effects of food through personalized dietary interventions based on these predictions.
Study of fibroblast growth factor 2 administration in bleomycin induced pulmonary fibrosis mice
Se Bi Lee,Hyeokku Lee,Jungyu Baek,Eunhyeok Choi,Hyunseung Lee,Juhyeok Hong,Jaehyun Kim,Jeong Yun Park,Gichang Jeong,Jieun Jeon,Jooyeon Lee,Jaehyun Park,Jimin Jang,Sang-Ryul Cha,Se-Ran Yang 한국실험동물학회 2023 한국실험동물학회 학술발표대회 논문집 Vol.2023 No.2