• 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.

• Journal of The Korean Association For Science Education
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• v.22 no.4
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• pp.862-871
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• 2002

We investigated elementary students' conceptions of the simple electric circuit using a battery, a bulb and a wire, and made comparison between the cognitive conflict through peer discussion and the cognitive conflict through physical experience. Two hundred and sixty-four sixth grade students who already had learned about the electric circuit were participated. The questionnaire to investigate the student's conceptions about simple electric circuit consisted of 5 items drawing the wire connections between a battery and a bulb to light the bulb. The students in the discussion group paired randomly with student who had different conceptions, and then each pairs discussed about their ideas freely with each other. After discussion they conducted CCLT(Cognitive Conflict Level Test) which consisted of 4 factors; recognition, interest, anxiety, reappraisal. The physical experience group conducted a task in which they connected a battery and a bulb with a wire, then conducted CCLT. The sixth graders had various misconceptions. Most students were not aware of the scope of negative battery terminal and two electric terminals of a bulb. Many students emphasized the tip of a bulb and positive battery terminal. The score of CCLT in the discussion group was higher than in the physical experience group. This results showed that discussion with peers was more effective than physical experience to arouse cognitive conflict.

• Korean Journal of Heritage: History & Science
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• v.51 no.3
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• pp.32-53
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• 2018

The $Sh{\bar{o}}s{\bar{o}}-in$(正倉院) is the detached storage building for Japanese treasures that belongs to $T{\bar{o}}dai-ji$ in Nara, Japan. The reason why $Sh{\bar{o}}s{\bar{o}}-in$ collections are drawing attention is that Japanese artifacts, sculptures, paintings, and other objects that were introduced through the Silk Road, such as Sasanian Persia and India, and those that were introduced from the Unified Silla and Tang China. In addition, hundreds of well-preserved documents of $Sh{\bar{o}}s{\bar{o}}-in$ collections play an important role as a historical reference material covering not only the social situation of the time but also the history of exchange of cultural diplomacy and the change of Buddhist doctrine. In particular, some of collections of $Sh{\bar{o}}s{\bar{o}}-in$ were made in China and may have been imported or received as gifts, but many of the artifacts made in Baekje and Unified Silla are becoming more and more important. This paper examined the research status of $Sh{\bar{o}}s{\bar{o}}-in$ metal crafts of Korean and foreign scholars, and examined the association with the relics of $Sh{\bar{o}}s{\bar{o}}-in$ through metal crafts excavated from the Korean Peninsula. The research on the future direction of $Sh{\bar{o}}s{\bar{o}}-in$ collections should be summarized as follows. 1. Systematization of state-level support and single window for the research of $Sh{\bar{o}}s{\bar{o}}-in$ collections 2. Accurate listing and database of $Sh{\bar{o}}s{\bar{o}}-in$ collections 3. The positive implementation of joint research with Japan and invitation of researchers related to $Sh{\bar{o}}s{\bar{o}}-in$ collections 4. The exchange exhibition between the Korean National Treasures and the $Sh{\bar{o}}s{\bar{o}}-in$ collections 5. Expansion of the research base through the publication and support of books related to $Sh{\bar{o}}s{\bar{o}}-in$ collections.

• Journal of Science Education
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• v.42 no.3
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• pp.371-389
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• 2018

This study focuses on the fact that students and teachers commonly have difficulty in 'selecting the topic' in many activities including student-led research that is conducted from topic selection to the drawing of conclusion. The purpose of this study is to develop a manual for science teaching research. The instructional manuals of 4 stages were developed based on practical knowledge that can be implemented in the actual class through previous research and literature. Each stage is composed of , , , and . In the third stage, students are expected to find scientific questions and develop them into research topics through detailed class research on newspaper articles, scientific magazines, traditional knowledge, proverbs, daily life, and textbook experiments. In the experimental group, the final research topic was selected through a variety of sources such as textbook experiments, proverbs, YouTube images, newspaper articles, individual WHY NOTEs, and understood the conditions of the scientific research topic and expressed the variables in the research title. However, in the control group, some students did not consider the research scope of the selected research subjects to be specific or not to be able to study at their level. As a result of giving the students as much autonomy as possible, many groups did not fully understand the previous research and submitted it. Based on the results of this study, it can be concluded that development and use of a 'topic selection stage' centered practice manual for general high school teachers would not only improve the students' abilities to discover solutions to scientific questions, but it will also help shift their attitudes towards science in a positive direction.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.1-25
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• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.