• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.780-786
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• 2015

Social media, such as Social Network Service include a lot of spontaneous opinions from customers, so recent companies collect and analyze information about customer feedback by using the system that analyzes Big Data on social media in order to efficiently operate businesses. However, it is difficult to analyze data collected from online sites accurately with existing morpheme analyzer because those data have spacing errors and spelling errors. In addition, many online sentences are short and do not include enough meanings which will be selected, so established meaning selection methods, such as mutual information, chi-square statistic are not able to practice Emotional Classification. In order to solve such problems, this paper suggests a module that can revise the meanings by using initial consonants/vowels and phase pattern dictionary and meaning selection method that uses priority of word class in a sentence. On the basis of word class extracted by morpheme analyzer, these new mechanisms would separate and analyze predicate and substantive, establish properties Database which is subordinate to relevant word class, and extract positive/negative emotions by using accumulated properties Database.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.305-311
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• 2022

This research aimed to provide a profound description of the practices of social media users in the Kingdom of Saudi Arabia (KSA), specifically the users of Facebook^® (FB) and Snapchat^® (SC), the reasons for these practices, decisions made, and the people involved. Such research would be of significant help to designers and policymakers of social media applications in understanding user practices when using social media applications and the reasons for such practices in the KSA. This better comprehension would be of significant help in improving current applications and creating new ones. According to the data analysis, there was a clear preference for SC over FB in the KSA. Most participants with SC accounts were described as very active users, accessing their accounts at least once a day compared to FB users. The users were led by this high preference for SC to create new words derived from the name of the application and use them in daily life. We showed our experience of carrying out a study in which the main objective was to collect factual empirical data from participants about their daily usage of social media applications while considering the unique cultural settings in the KSA. Mixed quantitative and qualitative methods were used to triangulate the data, increasing its trustworthiness and validity. Multiple perspectives were obtained using various data collection methods. Therefore, conclusions would not be confounded with limitations of any particular methodology or with conditions of any collection rounds. This research would constitute a valuable guide for researchers intending to use methods with male and female informants from different cultures, preparing them for potential challenges and suggesting possible solutions.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.207-226
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• 2019

Depression is a serious psychological disease that is expected to afflict an increasing number of people. And studies on depression have been conducted in the context of social media because social media is a platform through which users often frankly express their emotions and often reveal their mental states. In this study, large amounts of Korean text were collected and analyzed to determine whether such data could be used to detect depression in users. This study analyzed data collected from Twitter users who had and did not have depressive tendencies between January 2016 and February 2019. The data for each user was separately analyzed before and after the appearance of depressive tendencies to see how their expression changed. In this study the data were analyzed through co-occurrence word analysis, topic modeling, and sentiment analysis. This study's automated data collection method enabled analyses of data collected over a relatively long period of time. Also it compared the textual characteristics of users with depressive tendencies to those without depressive tendencies.

• Knowledge Management Research
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• v.18 no.4
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• pp.137-157
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• 2017

With the rapid growth of the internet, Social Network Services (SNSs) have played an important role. As the use of SNSs increases, users experience a sense of responsibility to respond to other people's messages or requests, and consequently experience an social overload, feeling too much social support for other users. In this study, we examined the effects of social overload on loneliness and SNS discontinuous usage intention. To verify the research model, data were collected from 83 SNS users and analyzed using SmartPLS, a structural equation modeling tool. The results of this study showed that the communal orientation and the degree of use of SNS influenced the social overload, and the social overload had a significant effect on loneliness and SNS discontinuous usage intention. The findings of this study are expected to help understand the social overload and loneliness in the use of SNS, and may also provide a strategic direction for SNS service providers.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.317-340
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• 2023

In a business context, ESG is defined as the use of environmental, social, and governance factors to assess a firm's progress in terms of sustainability. Social media has enabled the public to actively share firms' good and/or bad deeds, increasing public interest in ESG management. Therefore, this study aimed to investigate the association of firm performances with the respective sentiments towards each of environmental, social, and governance activities, as well as comprehensive ESG sentiments, which encompass all environmental, social, and governance sentiments. This study used panel regression models to examine the relationship between social media ESG sentiment and the Return on Assets (ROA) and Return on Equity (ROE) of 143 companies listed on the KOSPI 200. We collected data from 2018 to 2021, including sentiment data from a variety of social media channels, such as online communities, Instagram, blogs, Twitter, and other news. The results indicated that firm performance is significantly related to respective ESG and comprehensive ESG sentiments. This study has several implications. By using data from various social media channels, it presents an unbiased view of public ESG sentiment, rather than relying on ESG ratings, which may be influenced by rating agencies. Furthermore, the findings can be used to help firms determine the direction of their ESG management. Therefore, this study provides theoretical and practical insights for researchers and firms interested in ESG management.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.153-158
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• 2016

As the number of social network users increases, the information on event such as social issues and disasters receiving attention in each region is promptly posted by the bucket through social media site in real time, and its social ripple effect becomes huge. This study proposes a detection method of events that draw attention from users in specific region at specific time by using twitter data with regional information. In order to collect Twitter data, we use Twitter Streaming API. After collecting data, We implemented event detection system by analyze the frequency of a keyword which contained in a twit in a particular time and clustering the keywords that describes same event by exploiting keywords' co-occurrence graph. Finally, we evaluates the validity of our method through experiments.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.59-69
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• 2017

The best 'hub' to communicate with the citizen is using social media to marketing the business. However, there has several issued and the most common issue that face in critical is a capital issue. This issue is always highlight because most of automatic sentiment detection tool for Facebook or any other social media price is expensive and they lack of technical skills in order to control the tool. Therefore, in directly they have some obstacle to get faster product's feedback from customers. Thus, the personal online retailing need to struggle to stay in market because they need to compete with successful online company such as G-market. Sentiment analysis also known as opinion mining. Aim of this research is develop the tool that allow user to automatic detect the sentiment comment on social media account. RAD model methodology is chosen since its have several phases could produce more activities and output. Soppy tool will be develop using Microsoft Visual. In order to generate an accurate sentiment detection, the functionality testing will be use to find the effectiveness of this Soppy tool. This proposed automated Soppy Tool would be able to provide a platform to measure the impact of the customer sentiment over the postings on their social media site. The results and findings from the impact measurement could then be use as a recommendation in the developing or reviewing to enhance the capability and the profit to their personal online retailing company.

• Journal of Information Technology Applications and Management
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• v.26 no.6
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• pp.13-27
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• 2019

Social media are used as a tool which is suitable for delivering various images emotionally in the area of fashion. How deeply consumers are led by the brands to be engaged in the brands' SNS, how often they visit SNS and gain information, how much empathy they elicit from visitors with their contents and how continuously brands provide up-to-date information are the important factors to raise consumers' fashion consciousness and draw out their fashion consumption to express themselves. Therefore, this study aims to explore the effect of social media engagement and contents characteristics on fashion consumption tendency and purchase intention. In order to verify the research question, study makes analysis centering on the 2 × 2 × 2 MANCOVA model to draw out results of the differences among groups. As a result of analysis, this study verifies the difference between the effect of social media engagement on purchase intention and the effect of interaction of three variables on fashion consumption propensity and purchase intention and summarizes the implications.

• The Journal of Information Systems
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• v.23 no.4
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• pp.1-24
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• 2014

This study is intended to examine two issues related with social media messages. At first, the authors investigate that how they can categorize messages in the social media and how corporate twitters and brand twitters communicate with consumers. Secondly, after dividing messages in the social media into several groups, the authors investigate how each type of messages differ one another in terms of the consumer response. For examining these research issues, the authors gather twitter message data of global top 100 brands and categorize messages into 5 types (i.e., interactivity, diversion, information sharing, promotional, content) based on the motivation of communication and the format of the messages. Especially, the authors use content analysis methodology, which is normally used as the qualitative approach, in order to identify the type of messages. Furthermore, the authors present interactivity type of messages can communicate better with consumers and induce more favorable responses from consumers in the social media than any other type of messages. This research can provide implications in terms of theoretical, methodological, and managerial perspective.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.381-391
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• 2020

This research article investigates the way eWOM in social media influences the formation of destination image through development of trust and satisfaction for the potential tourist. The research involved administering an 18-point questionnaire taking online reviews, tourist involvement, and eWOM, destination image components of trust and satisfaction as variables. Data was collected from 554 individuals forming a cross-section of social media users and analyzed using multi-variate techniques (Reliability, CFA, and SEM). Results indicate a positive and significant relationship between all except online review and destination trust and satisfaction. Indirect and direct effects indicate that eWOM fully mediates the relationship between destination satisfaction and involvement and partially mediates the relationship between destination trust and involvement. In the case of online reviews, eWOM acts as a full mediator between destination trust and destination satisfaction for the future traveler using social media. The study proposes that components of image vary depending upon the degree of involvement, volume online reviews and eWOM generated also termed as 'virality' and these in turn influence the intention to revisit or recommend a destination. The study highlights its utility for National Tourist Organizations (NTOs) and online travel intermediaries to enhance destination marketing efforts.