• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.149-156
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• 2021

Text mining is one of the useful tools to discover public opinion and perception regarding political issues from big data. It is very common that users of social media express their opinion with newly-coined words such as slang and emoji. However, those new words are not effectively captured by traditional text mining methods that process text data using a language dictionary. In this study, we propose effective methods to extract newly-coined words that connote the political stance and opinion of users. With various text mining techniques, I attempt to discover the context and the political meaning of the new words.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.269-271
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• 2022

In recent years, RNN networks with LSTM blocks have been used extensively in machine learning tasks that process sequential data. These networks have proven to be particularly good at sequential language processing tasks by being more able to accurately predict the next most likely word in a given sequence than traditional neural networks. This study trained an RNN / LSTM neural network on three different translations of 150 biblical Psalms - in both English and Korean. The resulting model is then fed an input word and a length number from which it automatically generates a new Psalm of the desired length based on the patterns it recognized while training. The results of training the network on both English text and Korean text are compared and discussed.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.842-857
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• 2023

Currently, most sentiment classification models on microblogging platforms analyze sentence parts of speech and emoticons without comprehending users' emotional inclinations and grasping moral nuances. This study proposes a hybrid sentiment analysis model. Given the distinct nature of microblog comments, the model employs a combined stop-word list and word2vec for word vectorization. To mitigate local information loss, the TextCNN model, devoid of pooling layers, is employed for local feature extraction, while BiLSTM is utilized for contextual feature extraction in deep learning. Subsequently, microblog comment sentiments are categorized using a classification layer. Given the binary classification task at the output layer and the numerous hidden layers within BiLSTM, the Tanh activation function is adopted in this model. Experimental findings demonstrate that the enhanced TextCNN-BiLSTM model attains a precision of 94.75%. This represents a 1.21%, 1.25%, and 1.25% enhancement in precision, recall, and F1 values, respectively, in comparison to the individual deep learning models TextCNN. Furthermore, it outperforms BiLSTM by 0.78%, 0.9%, and 0.9% in precision, recall, and F1 values.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.935-945
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• 2021

The purpose of this study is to analyze the social perception of Arduino lecture using big data analysis method. For this purpose, data from January 2012 to May 2021 were collected using the Textom website as a keyword searched for 'arduino + lecture' in blogs, cafes, and news channels of NAVER website. The collected data was refined using the Textom website, and text mining analysis and semantic network analysis were performed by opening the Textom website, Ucinet 6, and Netdraw programs. As a result of text mining analysis such as frequency analysis, TF-IDF analysis, and degree centrality it was confirmed that 'education' and 'coding' were the top keywords. As a result of CONCOR analysis for semantic network analysis, four clusters can be identified: 'Arduino-related education', 'Physical computing-related lecture', 'Arduino special lecture', and 'GUI programming'. Through this study, it was possible to confirm various meaningful social perceptions of the general public in relation to Arduino lecture on the Internet. The results of this study will be used as data that provides meaningful implications for instructors preparing for Arduino lectures, researchers studying the subject, and policy makers who establish software education or coding education and related policies.

• Journal of Information Management
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• v.35 no.4
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• pp.1-16
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• 2004

In this paper, we outline the user interface problems that the text messaging users face to provide empirical basis for developing better improved mobile text messaging system. Our initial hypothesis was that the majority of the problems that the text messaging users face, namely, 1) difficulty in correctly understanding the intent of the incoming messages and 2) problem with frequently mis-addressing the recipient of the outgoing messages, can be accounted for by the poor usability of the text messaging user interface. Our analysis is based on the text message-based communication diaries, which were recorded for one week by each and every one of 75 college students, and survey taken from the same subjects. The data was collected in 2004. The students listed various difficulties including the limited message length, obscure input method, lack of mean to express emotional content, lack of receipt confirmation, lack of auto save feature when preparing messages to send, and lack of means to permanently save messages. Some of these problems were also identified in the previous studies. However, we were able to gather additional problems that the users face and also elicit potential solutions to remedy the problems. From these findings and analysis, we attempted to provide ways to improve the text messaging user interface.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.165-177
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• 2018

Recently, online commerces are becoming more common due to factors such as mobile technology development and smart device dissemination, and online review has a big influence on potential buyer's purchase decision. This study presents a set of analytical methodologies for understanding the meaning of customer reviews of products in online transaction. Using techniques currently developed in deep learning are implemented Hierarchical Attention Network for analyze meaning in online reviews. By using these techniques, we could solve time consuming pre-data analysis time problem and multiple topic problems. To this end, this study analyzes customer reviews of laptops sold in domestic online shopping malls. Our result successfully demonstrates over 90% classification accuracy. Therefore, this study classified the unstructured text data in the semantic analysis and confirmed the practical application possibility of the review analysis process.

• Journal of Fisheries and Marine Sciences Education
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• v.24 no.5
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• pp.643-652
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• 2012

The admission officer system that is discussed by research topic was introduced just six years ago. So, domestic journals for research trends analysis of admission officer system are not enough. From the time of the end of 1st government financial support project (2007~2012) for admission officer system, it is important things that to summarize the results of study and to look at the implications. The contents of this study will help that other researchers search the study directions. The implication of the research are as follows. First, we can see that range of research is widening and research is advancing. Second, the journals that related to high school will be come out more frequently. Finally, after accumulation of data, in-depth study analysis will be also come out.

• Journal of KIISE:Software and Applications
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• v.32 no.6
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• pp.502-511
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• 2005

In this paper, we propose a hybrid analysis method(HAM) based on gray-intensity information from natural scene images. The HAM is composed of GIA(Gray-intensity Information Analysis) and SMA(Split/Merge Analysis). Our experimental results show that the proposed approach is superior to conventional methods both in simple and complex images.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.300-305
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• 2024

Smart tourism enhances communication between tourists and residents, improves quality of life, increases the utilization of local tourism resources, and helps manage cities efficiently. This paper analyzes recent issues and trends in smart tourism, derives key factors for activating smart tourism based on the analyzed data, and conducts research on promoting smart tourism. Using smart tourism as a keyword, data was collected through Textom. The collection scope included a total of 33,588 pieces of data related to smart tourism over the past year, from May 1, 2023, to May 1, 2024. The data was analyzed using text mining and social network analysis techniques. Through this analysis, the paper suggests directions for the development of smart tourism, enabling the activation of local tourism and effective urban management.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.137-143
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• 2019

Fake news is a form of misinformation that has the advantage of rapid spreading of information on media platforms that users interact with, such as social media. There has been a lot of social problems due to the recent increase in fake news. In this paper, we propose a method to detect such false news. Previous research on fake news detection mainly focused on text analysis. This research focuses on a network where social media news spreads, generates qualities with DeepWalk, a network embedding method, and classifies fake news using logistic regression analysis. We conducted an experiment on fake news detection using 211 news on the Internet and 1.2 million news diffusion network data. The results show that the accuracy of false network detection using network embedding is 10.6% higher than that of text analysis. In addition, fake news detection, which combines text analysis and network embedding, does not show an increase in accuracy over network embedding. The results of this study can be effectively applied to the detection of fake news that organizations spread online.