Target-Aspect-Sentiment Joint Detection with CNN Auxiliary Loss for Aspect-Based Sentiment Analysis (CNN 보조 손실을 이용한 차원 기반 감성 분석)
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- Journal of Intelligence and Information Systems
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- v.27 no.4
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- pp.1-22
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- 2021
Aspect Based Sentiment Analysis (ABSA), which analyzes sentiment based on aspects that appear in the text, is drawing attention because it can be used in various business industries. ABSA is a study that analyzes sentiment by aspects for multiple aspects that a text has. It is being studied in various forms depending on the purpose, such as analyzing all targets or just aspects and sentiments. Here, the aspect refers to the property of a target, and the target refers to the text that causes the sentiment. For example, for restaurant reviews, you could set the aspect into food taste, food price, quality of service, mood of the restaurant, etc. Also, if there is a review that says, "The pasta was delicious, but the salad was not," the words "steak" and "salad," which are directly mentioned in the sentence, become the "target." So far, in ABSA, most studies have analyzed sentiment only based on aspects or targets. However, even with the same aspects or targets, sentiment analysis may be inaccurate. Instances would be when aspects or sentiment are divided or when sentiment exists without a target. For example, sentences like, "Pizza and the salad were good, but the steak was disappointing." Although the aspect of this sentence is limited to "food," conflicting sentiments coexist. In addition, in the case of sentences such as "Shrimp was delicious, but the price was extravagant," although the target here is "shrimp," there are opposite sentiments coexisting that are dependent on the aspect. Finally, in sentences like "The food arrived too late and is cold now." there is no target (NULL), but it transmits a negative sentiment toward the aspect "service." Like this, failure to consider both aspects and targets - when sentiment or aspect is divided or when sentiment exists without a target - creates a dual dependency problem. To address this problem, this research analyzes sentiment by considering both aspects and targets (Target-Aspect-Sentiment Detection, hereby TASD). This study detected the limitations of existing research in the field of TASD: local contexts are not fully captured, and the number of epochs and batch size dramatically lowers the F1-score. The current model excels in spotting overall context and relations between each word. However, it struggles with phrases in the local context and is relatively slow when learning. Therefore, this study tries to improve the model's performance. To achieve the objective of this research, we additionally used auxiliary loss in aspect-sentiment classification by constructing CNN(Convolutional Neural Network) layers parallel to existing models. If existing models have analyzed aspect-sentiment through BERT encoding, Pooler, and Linear layers, this research added CNN layer-adaptive average pooling to existing models, and learning was progressed by adding additional loss values for aspect-sentiment to existing loss. In other words, when learning, the auxiliary loss, computed through CNN layers, allowed the local context to be captured more fitted. After learning, the model is designed to do aspect-sentiment analysis through the existing method. To evaluate the performance of this model, two datasets, SemEval-2015 task 12 and SemEval-2016 task 5, were used and the f1-score increased compared to the existing models. When the batch was 8 and epoch was 5, the difference was largest between the F1-score of existing models and this study with 29 and 45, respectively. Even when batch and epoch were adjusted, the F1-scores were higher than the existing models. It can be said that even when the batch and epoch numbers were small, they can be learned effectively compared to the existing models. Therefore, it can be useful in situations where resources are limited. Through this study, aspect-based sentiments can be more accurately analyzed. Through various uses in business, such as development or establishing marketing strategies, both consumers and sellers will be able to make efficient decisions. In addition, it is believed that the model can be fully learned and utilized by small businesses, those that do not have much data, given that they use a pre-training model and recorded a relatively high F1-score even with limited resources.
This study was started to suggest the direction of Christian educational development to revitalize North Korea's 'education' research. Since the two Koreas have experienced heterogeneity in almost all elements of society, such as politics, economy, society, culture, and education, during the period of division in 1977, true unification depends on laying the foundation for social integration that can overcome the sense of heterogeneity between the two Koreas. This is why North Korea's "education" research is needed. Education is the foundation for transferring culture and history, and for bringing about the survival, transformation, and community of society and since it is the mission of Korean churches and Christian educators to establish the direction of North Korean "education" research, North Korean "education" research is very important. Despite this importance, 'North Korean research' in the field of Christian education has not been properly conducted. Research on the "Christian Unification Education Program" that can be used in churches is actively taking place, but research on the macro level of presenting post-unification education blueprints is rare. This study was started to suggest the direction of Christian educational development to revitalize North Korea's 'education' research. For the study, the characteristics of 'North Korea Research' were analyzed according to generational classification. As a result of the study, recent research on North Korea has been expanding in research topics and methodologies, and recent studies have been differentiated into microscopic studies that deviate from existing research trends at a macro level and view North Korea's daily life. The characteristics of 'North Korean education research' are summarized by period. The research on North Korean education, which began in earnest in the 1970s, was divided into the period of start(70s), transition(80s), leap(90s), expansion(2000s), and development(2010s~). and research characteristics for each period were analyzed. Through this, early North Korean education research was also conducted in the policy aspect of the country, and the characteristics of political and social studies were strong, but recent studies have confirmed that the subjects and contents are diversifying. Based on these studies, the pending issues and issues of North Korean education research in the field of Christian education were analyzed. The study of North Korea in the field of Christian education, which began in the 1980s, has been conducted in the engineering aspect of 'development of unification education programs for churches'. However, studies on Christian unification education and North Korean education itself, which can be used in public education sites including Christian schools, have yet to be sufficient. Nevertheless, the diversification of research in the field of Christian education can be evaluated as a positive change. Based on these studies, it was proposed to establish a de-ideological research foundation, secure primary research data(Raw Data), activate research topics and research methodologies, and strengthen research capabilities in the direction of development to revitalize North Korean research in the field of Christian education. I hope this study will trigger various follow-up studies and help Korean churches that must achieve unification.
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (