• Asia pacific journal of information systems
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• v.32 no.2
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• pp.226-248
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• 2022

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.

• Journal of Korean Society of Transportation
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• v.9 no.2
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• pp.75-86
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• 1991

A conventional track structure system of rails ties ballast subballast and subgrade. An adequate engineering analysis require consideration of all major components of track system. This paper briefly reviews existing methods of track analysis and presents a new model based of the examination of the Tallbot Model. The new model has been validated by comparision with settlements from other model.

• Knowledge Management Research
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• v.21 no.1
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• pp.197-210
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• 2020

The smart speaker includes an AI assistant function in the existing portable speaker, which enables a person to give various commands using a voice and provides various offline services associated with control of a connected device. The speed of domestic distribution is also increasing, and the functions and linked services available through smart speakers are expanding to shopping and food orders. Through text mining-based customer review analysis, there have been many proposals for identifying the impact on customer attitudes, sentiment analysis, and product evaluation of product functions and attributes. Emotional investigation has been performed by extracting words corresponding to characteristics or features from product reviews and analyzing the impact on assessment. After obtaining the topic from the review, the effect on the evaluation was analyzed. And the market competition of similar products was visualized. Also, a study was conducted to analyze the reviews of smart speaker users through text mining and to identify the main attributes, emotional sensitivity analysis, and the effects of artificial intelligence attributes on product satisfaction. The purpose of this study is to collect blog posts about the user's experiences of smart speakers released in Korea and to analyze the attitudes of customers according to their attributes. Through this, customers' attitudes can be identified and visualized by each smart speaker product, and the positioning map of the product was derived based on customer recognition of smart speaker products by collecting the information identified by each property.

• Journal of Information Technology Services
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• v.19 no.3
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• pp.35-56
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• 2020

The purpose of this study is to measure corporate personality by analyzing the internal employees' corporate reviews and to identify the impact of the representative corporate personality on the relationship between job satisfaction of internal employees and the turnover rate of the company. To this end, we first created a dictionary of words representing the corporate personality with a Word2vec method based on words explaining five corporate personalities, such as reliability, initiative, practicality, activism, and femininity, obtained from the preceding study. Next, we analyzed reviews which were written by internal employees on their companies to measure the score of corporate personality at a review level, aggregated the review level scores for each company to calculate the company level score of corporate personality, and assigned to each company the corporate personality with the maximum score among the five such scores. Also, job satisfaction and turnover rate were measured from internal employees' corporate evaluation scores and the percentage of former employees of each company who left a review on the company, respectively. This study collected datasets of corporate reviews, employee information, and corporate information from Job-Planet from 2014 to 2017, conducted a technical statistic check and correlation analysis to confirm the suitability of the datasets, and performed linear regression analysis to evaluate the research model and verify hypotheses. As a result of the analysis, the job satisfaction of the internal staff has a significant negative impact on the corporate's turnover rate. In addition, companies having a personality of reliability, initiative and femininity also showed a significant cause-and-effect relationship between job satisfaction and turnover rate and among them, job satisfaction of companies having a personality, initiative, showed a greater impact on turnover rate. In sum, we not only proposed a novel method of measuring corporate personality, but also showed that corporates need to identify its corporate personality and to utilize a different strategy to reduce their employee's turnover rate depending on the corporate personality.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.279-290
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• 2022

As the growth of the live streaming service market is accelerating due to COVID-19, the number of downloads and reviews of live streaming mobile applications is also rapidly skyrocketing. This study is to research game streaming applications using Twitch reviews as database. A total of 8 topics are extracted through LDA topic modeling and 7 out of them are detected to be inconvenience factors. Then, to pinpoint the main inconvenience factors, co-occurrence analysis is used in order to find out main factors. Finally, based on previous studies, several solutions are provided, which can solve the inconvenience factors(advertisement, UI design, technology problems) as well as strengthening digital competitiveness. This study will serve as an opportunity to improve digital competitiveness not only for Twitch but also for other game live streaming service companies in the future.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.201-207
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• 2022

In this paper, we propose a movie recommendation model that uses the users' ratings as well as their reviews. To understand the user's preference from multicriteria perspectives, the proposed model is designed to apply attribute-based sentiment analysis to the reviews. For doing this, it divides the reviews left by customers into multicriteria components according to its implicit attributes, and applies BERT-based sentiment analysis to each of them. After that, our model selectively combines the attributes that each user considers important to CF to generate recommendation results. To validate usefulness of the proposed model, we applied it to the real-world movie recommendation case. Experimental results showed that the accuracy of the proposed model was improved compared to the traditional CF. This study has academic and practical significance since it presents a new approach to select and use models in consideration of individual characteristics, and to derive various attributes from a review instead of evaluating each of them.

• Journal of Korean Medicine Rehabilitation
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• v.32 no.2
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• pp.83-94
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• 2022

Objectives This systematic review aimed to analyze research about pelvic deviation diagnosis for Chuna manual therapy (CMT) and to review the diagnosis methods, indices, and results of diagnosis. Methods Ten electronic databases were systematically searched up to January 4th 2022. Clinical studies and reviews containing pelvic deviation diagnosis for CMT or using CMT as a treatment of pelvic deviation were selected and evaluated. CMT diagnosis in clinical studies and reviews were isolated and analyzed by 2 independent reviewers. Results Thirteen clinical studies and three reviews were included in the evaluation. X-ray analysis and manual testing were the two main methods used in CMT diagnosis of pelvic deviation. For manual testing in clinical studies, leg length insufficiency testing was the most frequently used measurement index and the most common diagnostic results were anterior and posterior rotation. In the X-ray analysis, Obturator foramen and femur head line were the most frequently used measurement index and the most common diagnostic results were anterior rotation and posterior rotation. Conclusions The systematic review found that manual testing and X-ray analysis were mainly used for the diagnosis of pelvic deviation in CMT among clincial and review articles. As there was little research about diagnosing pelvic deviation in CMT and any existing research presented only low standards of evidence, further research should be updated with using a more standardized approach.

• The Journal of the Korea Contents Association
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• v.14 no.5
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• pp.85-94
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• 2014

This study examined the characteristics of movie reviews based on emotional expressions, using the structural analysis. Major results were as follows; firstly, the most cited emotional expression was 'fun'. Fun was the important discriminator for evaluating movies. Secondly, cluster analysis results found that although Korean movies were clustered by many emotional expressions such as fun, immersion and impression, foreign movies were grouped by joust an emotional expression including fun. Internet users tended to divide foreign movie into two kinds of movies such as fun movie and boring movies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.231-244
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• 2022

With the improvement of people's living standards and the development of tourism, tourists have greater freedom in choosing destinations. Therefore, as an indicator of satisfaction with scenic spots, tourist comments are becoming increasingly prominent. This paper aims to compare and analyze the landscape image of the Five Great Mountains in China and provide specific strategies for its development. The online reviews of tourists on the Online Travel Agency (OTA) website about the Five Great Mountains from 2015 to 2018 are collected as research samples. The text analysis method and R language are used to analyze the content of the tourist reviews, while the high-frequency words in the word cloud are used for visual display. In addition, the entropy weight method is used to determine the index weight and tourist satisfaction is evaluated to understand the weaknesses of those scenic spots. The results of the study show that firstly, the tourist satisfaction with the Five Great Mountains is basically consistent with its popularity. Secondly, through weight analysis, tourists pay special attention to the landscape features and environmental health of the scenic area, so that relevant departments should focus on building the landscape characteristics and improving the environmental health of the scenic area. At the same time, the accommodation and service management of the scenic spot cannot be ignored. Finally, according to the analysis results, suggestions are made on how to improve the tourist satisfaction with the Five Great Mountains.

• The Journal of Information Systems
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• v.31 no.2
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• pp.239-261
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• 2022

Purpose The purpose of this study is to explore and compare changes in tourist opinion during the COVID-19 pandemic. Since the COVID-19 outbreak has caused changes in all areas of our lives, the conditions related to confinement during a lockdown have led to changes in tourists' habits and behaviors. Design/methodology/approach To analyze opinion changes about tourist attractions, this study performed topic modeling by summarizing topics into five dimensions: management, scenery, price, suggestion, and safety; then, based on the topic modeling results, sentiment analysis and emotion analysis were conducted to explore the change of tourists' opinion during the COVID-19 pandemic. Findings According to the results, this study confirmed the pandemic's positive effect on tourists' opinions about attractions after the COVID 19 outbreak. Presumably due to the absence of lines and crowed. Moreover, the dimension 'Safety' started to appear in US tourists' attractions reviews only in the period after the outbreak and during the mass vaccination. These results mean that tourists started to care more about safety due to the impact of the COVID-19 pandemic.