• International Journal of Internet, Broadcasting and Communication
• /
• v.13 no.1
• /
• pp.61-68
• /
• 2021

The development of the fourth industry has enabled users to quickly share a lot of data online. We can analyze big data on information about tourist attractions and users' experiences and opinions using artificial intelligence. It can also analyze the association between characteristics of users and types of tourism. This paper analyzes individual characteristics, recommends customized tourist sites and proposes a system to provide the sacred texts of recommended tourist sites as AR services. The system uses machine learning to analyze the relationship between personality type and tourism type preference. Based on this, it recommends tourist attractions according to the gender and personality types of users. When the user finishes selecting a tourist destination from the recommendation list, it visualizes the information of the selected tourist destination with AR.

• Journal of the Architectural Institute of Korea Planning & Design
• /
• v.35 no.5
• /
• pp.21-30
• /
• 2019

The purpose of this study is to present the direction of the new space plan by reflecting the opinions of the user (student) in the existing standardized elementary school space planning. The purpose of this study is to investigate the activities of elementary school students by using socio - spatial network analysis method and to propose the direction of new elementary school space planning through the results. We analyzed the results of each centrality by using the analysis of closeness analysis, betweeness analysis, girvan-newman clustering, and concor analysis. The results of this study are as follows. First, it should be planned to use the classroom and the special room as one area by utilizing the corridor. Second, it should be planned that the outdoor space and the indoor space are closely related to each other by utilizing the hall, the lobby and the classroom. Third, the school should create a small space where physical activity is possible in an indoor space of the school. In order to improve the standardized elementary school space, this study proposes a method to reflect the opinions of the users in the school planning stage.

• Journal of the Architectural Institute of Korea Planning & Design
• /
• v.35 no.10
• /
• pp.19-30
• /
• 2019

The purpose of this study is to explore ways to efficiently capture the opinions of students who are main users in the planning, planning and design of school facilities. There are many criteria for determining good schools, but in this study, the main purpose of this study was to set up the main users as students, and to analyze the differences between the network and the actual spatial structure (network) created by the hope of students' use of school space, and the direction to overcome the differences. After surveying students' opinions about their satisfaction with school space and their desire to use school space by limiting the survey target to middle and high schools in Seoul, the 'social-space network analysis' was recently established in the social science field. As a result, it was found that the proximity of space and space desired by students in the school varied greatly depending on the geographical conditions, school districts, and the status of the current facilities, and the direction of improvement specialized for each school was found.

• Journal of Information Technology Applications and Management
• /
• v.23 no.2
• /
• pp.207-224
• /
• 2016

Investments in information and communication technologies (ICT) around the world have grown at an enormous rate over the past two decades, which reflects a new emphasis on consumer mobile devices. A social network service (SNS) is an online service that aims to build social relations among people who share interests and activities. The role of SNS is enormous for communicating ideas and opinions among social participants. The use of SNS has recently become one of the most popular social activities worldwide. This research investigated relation between personal characteristics, social characteristics and user satisfaction on SNS then, analyzed how these factors affecting continuance usage intention on SNS users. The conclusion is summarized as below. The study results show that informativeness, pleasure, innovativeness, relationship and empathy of SNS are having positive impact to some degree on the user satisfaction. Further, the user satisfaction of SNS users and quality of life have a positive impact on the continuance usage intention of SNS users. This results show that various SNS qualities are necessary to actively explore and obtain further information that users intend to find, while they are insufficient in function to provide the information other users require or exchange information with other users through the SNS.

• International Journal of Advanced Culture Technology
• /
• v.9 no.2
• /
• pp.86-90
• /
• 2021

Sentiment refers to a person's thoughts, opinions, and feelings toward an object. Sentiment analysis is a process of collecting opinions on a specific target and classifying them according to their emotions, and applies to opinion mining that analyzes product reviews and reviews on the web. Companies and users can grasp the opinions of public opinion and come up with a way to do so. Recently, natural language processing models using the Transformer structure have appeared, and Google's BERT is a representative example. Afterwards, various models came out by remodeling the BERT. Among them, the Facebook AI team unveiled the XLM-R (XLM-RoBERTa), an upgraded XLM model. XLM-R solved the data limitation and the curse of multilinguality by training XLM with 2TB or more refined CC (CommonCrawl), not Wikipedia data. This model showed that the multilingual model has similar performance to the single language model when it is trained by adjusting the size of the model and the data required for training. Therefore, in this paper, we study the improvement of Korean sentiment analysis performed using a pre-trained XLM-R model that solved curse of multilinguality and improved performance.

• International Journal of Contents
• /
• v.15 no.4
• /
• pp.74-81
• /
• 2019

This study examined the overall relationship between information privacy concern, need for uniqueness (NFU), and disclosure behavior to explain the personal factors that drive data-sharing on Facebook. The results of an online survey conducted with 222 Facebook users show that among diverse data that social media users disclose online, four distinct factors are identified: basic personal data, private data, personal opinions, and personal photos. In general, there is a negative relationship between privacy concern and a positive relationship between the NFU and the willingness to self-disclose information. Overall, the NFU was a better predictor of willingness to disclose information than privacy concern, gender, or age. While privacy concern has been identified as an influential factor when users evaluate social networking sites, the findings of this study contribute to the literature by demonstrating that an individual's need to manifest individualization on social media overrides privacy concerns.

• Journal of the Korean Institute of Plant Engineering
• /
• v.23 no.4
• /
• pp.57-64
• /
• 2018

User-based collaborative filtering is a method of recommending an item to a user based on the preference of the neighbor users who have similar purchasing history to the target user. User-based collaborative filtering is based on the fact that users are strongly influenced by the opinions of other users with similar interests. Item-based collaborative filtering is a method of recommending an item by comparing the similarity of the user's previously preferred items. In this study, we create a recommendation model using user-based collaborative filtering and item-based collaborative filtering with consumer's consumption data. Collaborative filtering is performed by using RFM (recency, frequency, and monetary) technique with purchasing data to recommend items with high purchase potential. We compared the performance of the recommendation system with the purchase amount and the performance when applying the RFM method. The performance of recommendation system using RFM technique is better.

• International Journal of Computer Science & Network Security
• /
• v.21 no.1
• /
• pp.174-183
• /
• 2021

The advent of social media has revolutionized the speed of communication between millions of people around the world in various cultures and disciplines. Social media is the best platform for exchanging opinions and ideas, interacting with other users of similar interests and sharing different types of media and files. With the phenomenal increase in the use of social media platforms, the need to pay attention to protection and security from attacks and misuse has also increased. The present study conducts a comprehensive survey of the latest and most important research studies published from 2018-20 on security and privacy on social media and types of threats and attacks that affect the users. We have also reviewed the recent challenges that affect security features in social media. Furthermore, this research pursuit also presents effective and feasible solutions that address these threats and attacks and cites recommendations to increase security and privacy for the users of social media.

• Science of Emotion and Sensibility
• /
• v.15 no.1
• /
• pp.97-104
• /
• 2012

Recently, a big text data has been produced by users, an opinion mining to analyze information and opinion about users is becoming a hot issue. Of the opinion mining, especially a sentiment analysis is a study for analysing emotions such as a positive, negative, happiness, sadness, and so on analysing personal opinions or emotions for commercial products, social issues and opinions of politician. To analyze the sentiment analysis, previous studies used a mapping method setting up a distribution of emotions using two dimensions composed of a valence and arousal. But previous studies set up a distribution of emotions arbitrarily. In order to solve the problem, we composed a distribution of 12 emotions through carrying out a survey using Korean emotion words list. Also, certain emotional states on two dimension overlapping multiple emotions, we proposed a selection method with Roulette wheel method using a selection probability. The proposed method shows to classify a text into emotion extracting emotion terms from a text.

• The Journal of Society for e-Business Studies
• /
• v.18 no.4
• /
• pp.315-326
• /
• 2013

Users in social media post various types of opinions such as product reviews and movie reviews. It is a common trend that customers get assistance from the opinions in making their decisions. However, as opinion usage grows, distorted feedbacks also have increased. For example, exaggerated positive opinions are posted for promoting target products. So are negative opinions which are far from common evaluations. Finding these biased opinions becomes important to keep social media reliable. Techniques of opinion mining (or sentiment analysis) have been developed to determine sentiment polarity of opinionated documents. These techniques can be utilized for finding the biased opinions. However, the previous techniques have some drawback. They categorize the text into only positive and negative, and they also need a large amount of training data to build the classifier. In this paper, we propose methods for discovering the biased opinions which are skewed from the overall common opinions. The methods are based on angle based outlier detection and personalized PageRank, which can be applied without training data. We analyze the performance of the proposed techniques by presenting experimental results on a movie review dataset.