• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.559-566
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• 2007

Subjective sentences express opinions, emotions, evaluations and other subjective ideas relevant to products or events. These expressions sometimes can be seen in only part of a sentence, thus extracting features from a full-sentence can degrade the performance of subjective-sentence-classification. This paper presents a method for improving the performance of a subjectivity classifier by combining two classifiers generated from the different representations of an input sentence. One representation is a sentimental phrase that represents an automatically identified subjective expression or objective expression and the other representation is a full-sentence. Each representation is used to extract modified n-grams that are composed of a word and its contextual words' polarity information. The best performance, 79.7% accuracy, 2.5% improvement, was obtained when the phrase-level classifier and the sentence-level classifier were merged.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.9-15
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• 2019

Large global online shopping malls, such as Amazon, offer services in English or in the language of a country when their products are sold. Since many customers purchase products based on the product reviews, the shopping malls actively utilize the sentimental analysis technique in judging preference of each product using the large amount of review data that the customer has written. And the result of such analysis can be used for the marketing to look the potential shoppers. However, it is difficult to apply this English-based semantic analysis system to different languages used around the world. In this study, more than 500,000 data from Amazon fine food reviews was used for training a deep learning based system. First, sentiment analysis evaluation experiments were carried out with three models of English test data. Secondly, the same data was translated into seven languages (Korean, Japanese, Chinese, Vietnamese, French, German and English) and then the similar experiments were done. The result suggests that although the accuracy of the sentimental analysis was 2.77% lower than the average of the seven countries (91.59%) compared to the English (94.35%), it is believed that the results of the experiment can be used for practical applications.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.29-43
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• 2021

Coinciding with the advent of COVID-19, online education is on the rise both domestically and globally, and has become an absolutely necessary and irreplaceable form of education. It is a very curious question what the perception of people about the suddenly growing form of education is, and how it has changed. This study investigated changes in consumers' perception of online education using big data. To this end, we divided the time into four stages: before COVID-19 (November to December 2019), after the triggering of COVID-19 (January to February 2020), right after the online classes started (March to April 2020), after experiencing some online education (May to June 2020). Then we conducted text mining, namely, keyword frequency analysis, network analysis, word cloud analysis, and sentiment analysis were performed. The implications derived as a result of the analysis can help education policy makers and educators working in the field to improve online education quality and establish its future directions.

• Sijohaknonchong
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• v.42
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• pp.93-115
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• 2015

Dynamics of Sijo was regarded as a genre of 'closed nature' once. But ironically thanks to the 'closed nature', today dynamics of Sijo is refocused in diverse fields. Sijo is quoted not only in its original field of literature, but also in writing study. Quite remarkably, it is often referred to in the field of literary therapy, further in emotional healing. This article discussed the dynamics of Sijo as a manifestation of emotion especially called Gamsung in the process of the refocus. It is to show effectively that as a literary genre, Sijo can interact and share what is reasonal as well as what is emotional and sentimental in a poem as an emotional container beyond the lyricism Sijo has. Of course, it is also clear that the concept of lyricism may limit the dynamics of Sijo itself. Thus, the key word 'Gamsung' mainly referred to in this article was used to show the dynamics which Sijo has as much as possible, overcoming the limitation. That is, the purpose of the study is to prove that Sijo is the genre to represent human emotion most dynamically by reviewing the reasonal aspect of Sijo in addition to its emotional disposition which has been estimated to focus on sentiment or emotion. In the process of reinterpreting the structure of Sijo, the specific analysis on such emotional disposition and reasonal aspect was conducted by structurizing that as '(1) Facing, (2) Feeling dynamical, (3) Interpellating by feeling, and (4) feeling by sensation'.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.161-177
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• 2019

In this paper, we study the performance improvement of the answer extraction in Question-Answering system by using sentence dependency parsing result. The Question-Answering (QA) system consists of query analysis, which is a method of analyzing the user's query, and answer extraction, which is a method to extract appropriate answers in the document. And various studies have been conducted on two methods. In order to improve the performance of answer extraction, it is necessary to accurately reflect the grammatical information of sentences. In Korean, because word order structure is free and omission of sentence components is frequent, dependency parsing is a good way to analyze Korean syntax. Therefore, in this study, we improved the performance of the answer extraction by adding the features generated by dependency parsing analysis to the inputs of the answer extraction model (Bidirectional LSTM-CRF). The process of generating the dependency graph embedding consists of the steps of generating the dependency graph from the dependency parsing result and learning the embedding of the graph. In this study, we compared the performance of the answer extraction model when inputting basic word features generated without the dependency parsing and the performance of the model when inputting the addition of the Eojeol tag feature and dependency graph embedding feature. Since dependency parsing is performed on a basic unit of an Eojeol, which is a component of sentences separated by a space, the tag information of the Eojeol can be obtained as a result of the dependency parsing. The Eojeol tag feature means the tag information of the Eojeol. The process of generating the dependency graph embedding consists of the steps of generating the dependency graph from the dependency parsing result and learning the embedding of the graph. From the dependency parsing result, a graph is generated from the Eojeol to the node, the dependency between the Eojeol to the edge, and the Eojeol tag to the node label. In this process, an undirected graph is generated or a directed graph is generated according to whether or not the dependency relation direction is considered. To obtain the embedding of the graph, we used Graph2Vec, which is a method of finding the embedding of the graph by the subgraphs constituting a graph. We can specify the maximum path length between nodes in the process of finding subgraphs of a graph. If the maximum path length between nodes is 1, graph embedding is generated only by direct dependency between Eojeol, and graph embedding is generated including indirect dependencies as the maximum path length between nodes becomes larger. In the experiment, the maximum path length between nodes is adjusted differently from 1 to 3 depending on whether direction of dependency is considered or not, and the performance of answer extraction is measured. Experimental results show that both Eojeol tag feature and dependency graph embedding feature improve the performance of answer extraction. In particular, considering the direction of the dependency relation and extracting the dependency graph generated with the maximum path length of 1 in the subgraph extraction process in Graph2Vec as the input of the model, the highest answer extraction performance was shown. As a result of these experiments, we concluded that it is better to take into account the direction of dependence and to consider only the direct connection rather than the indirect dependence between the words. The significance of this study is as follows. First, we improved the performance of answer extraction by adding features using dependency parsing results, taking into account the characteristics of Korean, which is free of word order structure and omission of sentence components. Second, we generated feature of dependency parsing result by learning - based graph embedding method without defining the pattern of dependency between Eojeol. Future research directions are as follows. In this study, the features generated as a result of the dependency parsing are applied only to the answer extraction model in order to grasp the meaning. However, in the future, if the performance is confirmed by applying the features to various natural language processing models such as sentiment analysis or name entity recognition, the validity of the features can be verified more accurately.