• The Journal of the Korea Contents Association
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• v.14 no.8
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• pp.416-430
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• 2014

As the online word-of-mouth(WOM) using SNS has significant influence on consumer decision-making, the hospitality industry including the restaurant industry has actively used SNSs as one of major marketing tools. While researchers have focused on impacts of the online WOM, there is little research on motivations to provide WOM and its impacts on the WOM providers. The purpose of this study is to examine whether sharing the restaurant experience on Facebook, the representative SNSs, can change customer satisfaction and intentions to revisit and recommended and whether the type of motivations to share the restaurant experiences on Facebook affects customer satisfaction and intentions to revisit and recommend. The total of 260 college students volunteered to participate in this study. They first visited a restaurant and completed surveys twice before and after sharing their restaurant experience on Facebook. According to the study results, the levels of satisfaction, intention to revisit and intention to recommend after sharing the restaurant experience were found to be higher than before sharing the experience. This study also found that people who shared their restaurant experience for nostalgia were more likely to be satisfied with the restaurant services and have a higher level of intentions to revisit and recommend the restaurant. Theoretical and managerial implications as well as limitations and future research directions are discussed.

• Korean journal of communication and information
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• v.58
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• pp.7-30
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• 2012

The advent of Social Journalism coincided with the rise of social media to create and deliver news information; as a type of civic journalism, social journalism may be characterized as a new form of information gathering and news reporting which is fed by citizens creating news information through their use social networking services (SNSs). The current study analyzed a Facebook page called, to determine how this page was utilized during the onset of the citizen movement for the Egyptian democratic revolution to produce news, to facilitate interaction among the public and to deliver the news under the form of networked journalism. Each post uploaded onto the Facebook page from January 27 till February 2, 2011 was coded in its category, content and the contextual frame of the news. The results of the study showed that during the first week, straight news rather than those with opinions was produced most frequently. The research findings of the current study suggest that in a society of political turmoil, such as in Egypt and other Arabic countries, when the institutionalized media are controlled severely by the government or other forces, SNSs can perform journalistic media roles which create and distribute news information representing facts and reality, and simultaneously facilitate the public's interactions on social and political issues.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.112-119
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• 2023

Traditionally used for networking computers and communications, the Internet has been evolving from the beginning. Internet is the backbone for many things on the web including social media. The concept of social networking which started in the early 1990s has also been growing with the internet. Social Networking Sites (SNSs) sprung and stayed back to an important element of internet usage mainly due to the services or provisions they allow on the web. Twitter and Facebook have become the primary means by which most individuals keep in touch with others and carry on substantive conversations. These sites allow the posting of photos, videos and support audio and video storage on the sites which can be shared amongst users. Although an attractive option, these provisions have also culminated in issues for these sites like posting offensive material. Though not always, users of SNSs have their share in promoting hate by their words or speeches which is difficult to be curtailed after being uploaded in the media. Hence, this article outlines a process for extracting user reviews from the Twitter corpus in order to identify instances of hate speech. Through the use of MPCA (Modified Principal Component Analysis) and ECNN, we are able to identify instances of hate speech in the text (Enhanced Convolutional Neural Network). With the use of NLP, a fully autonomous system for assessing syntax and meaning can be established (NLP). There is a strong emphasis on pre-processing, feature extraction, and classification. Cleansing the text by removing extra spaces, punctuation, and stop words is what normalization is all about. In the process of extracting features, these features that have already been processed are used. During the feature extraction process, the MPCA algorithm is used. It takes a set of related features and pulls out the ones that tell us the most about the dataset we give itThe proposed categorization method is then put forth as a means of detecting instances of hate speech or abusive language. It is argued that ECNN is superior to other methods for identifying hateful content online. It can take in massive amounts of data and quickly return accurate results, especially for larger datasets. As a result, the proposed MPCA+ECNN algorithm improves not only the F-measure values, but also the accuracy, precision, and recall.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1391-1399
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• 2015

In recent times, studies sentiment analysis are being actively conducted by implementing natural language processing technologies for analyzing subjective data such as opinions and attitudes of users expressed on the Web, blogs, and social networking services (SNSs). Conventionally, to classify the sentiments in texts, most studies determine positive/negative/neutral sentiments by assigning polarity values for sentiment vocabulary using sentiment lexicons. However, in this study, sentiments are classified based on Thayer's model, which is psychologically defined, unlike the polarity classification used in opinion mining. In this paper, as a method for classifying the sentiments, sentiment categories are proposed by extracting sentiment keywords for major sentiments by using hashtags, which are essential elements of Instagram. By applying sentiment categories to user posts, sentiments can be determined through the similarity measurement between the sentiment adjective candidates and the sentiment keywords. The test results of the proposed method show that the average accuracy rate for all the sentiment categories was 90.7%, which indicates good performance. If a sentiment classification system with a large capacity is prepared using the proposed method, then it is expected that sentiment analysis in various fields will be possible, such as for determining social phenomena through SNS.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.3
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• pp.59-71
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• 2018

Social media has immense popularity among all services today. Data from social network services (SNSs) can be used for various objectives, such as text prediction or sentiment analysis. There is a great deal of Korean and English data on social media that can be used for sentiment analysis, but handling such huge amounts of unstructured data presents a difficult task. Machine learning is needed to handle such huge amounts of data. This research focuses on predicting Korean and English sentiment using deep forward neural network with a deep learning architecture and compares it with other methods, such as LDA MLP and GENSIM, using logistic regression. The research findings indicate an approximately 75% accuracy rate when predicting sentiments using DNN, with a latent Dirichelet allocation (LDA) prediction accuracy rate of approximately 81%, with the corpus being approximately 64% accurate between English and Korean.

• Journal of Fashion Business
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• v.21 no.3
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• pp.67-79
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• 2017

This study investigated consumers' enjoyment, perceived risks, expected values, and innovativeness factors and the effects of the convenience and personalization of "online to offline" (O2O) fashion mobile commerce on its perceived usefulness, perceived ease of use, and consumers' attitudes and intention to use O2O mobile commerce. A research model was developed using the Technology Acceptance Model (TAM). A mobile survey was conducted through smartphone messengers and SNSs targeting male and female college students in their 20s who are living in the Seoul Metropolitan Area. A total of 192 questionnaire responses were used in the analysis. "Among the consumer characteristics, only enjoyment and expected values were found to make consumers feel that the O2O fashion mobile commerce is useful and easy to use. Among the mobile commerce characteristics, only convenience was found to have significant effects on consumers' perceived usefulness and ease of use regarding O2O fashion mobile commerce. Perceived usefulness was found to have the effects on attitudes as well as intention to use toward O2O mobile commerce. It was shown that positive attitudes toward O2O mobile commerce led to positive use intention toward O2O mobile commerce.

• Journal of Korea Multimedia Society
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• v.16 no.11
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• pp.1297-1304
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• 2013

The quick report and propagate information are increasingly important because nowadays it is hard to predict the damages of flooding by unexpected heavy rain. In addition, there are not many ways to receive disaster information in real time. Accordingly, we designed the system which can earn information from a lot of messages on twitter. Above all, our system can extract and deploy disaster information by comparison with erstwhile social network service mash-up system as only broadcast media. Significant objective of this paper is to design the fastest extract disaster information system of mass media.

• Journal of Multimedia Information System
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• v.2 no.3
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• pp.287-294
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• 2015

We are living in epoch of information when Internet touches all aspects of our lives. Therefore, it provides a plenty of services each of which benefits people in different ways. Electronic Mail (E-mail), File Transfer Protocol (FTP), Voice/Video Communication, Search Engines are bright examples of Internet services. Between them Social Network Services (SNS) continuously gain its popularity over the past years. Most popular SNSs like Facebook, Weibo and Twitter generate millions of data every minute. Twitter is one of SNS which allows its users post short instant messages. They, 100 million, posted 340 million tweets per day (2012)[1]. Often big amount of data contains lots of noisy data which can be defined as uninteresting and unclassifiable data. However, researchers can take advantage of such huge information in order to analyze and extract meaningful and interesting features. The way to collect SNS data as well as tweets is handled by crawlers. Twitter crawler has recently emerged as a great tool to crawl Twitter data as well as tweets. In this project, we develop Twitter Crawler system which enables us to extract Twitter data. We implemented our system in Java language along with MySQL. We use Twitter4J which is a java library for communicating with Twitter API. The application, first, connects to Twitter API, then retrieves tweets, and stores them into database. We also develop crawling strategies to efficiently extract tweets in terms of time and amount.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.33-42
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• 2016

Recently, an environment in which public opinions are expressed about various areas is expanded around SNSs or internet potals, thus, opinion documents get bigger rapidly. Under these circumstances, it is essential to utilize automatic summarization techniques for understanding whole contents of large opinion documents. However, it is hard to summarize efficiently those documents with traditional text summarization technologies since the documents include subject expressions as well as features of targets objects. Proposed method in this paper defines features of opinion documents, and designed to retrieve representative sentences expressing opinions of those features. In addition, through experiments, we prove the usefulness of proposed method.

• Journal of Advanced Navigation Technology
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• v.14 no.5
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• pp.737-744
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• 2010

A Social Network Service(SNS) is the best attracting internet business model and serviced in the several countries. The Facebook is the most popular SNS in a foreign country and the Cyworld is most popular one in Korea. In this paper, after investing and analyzing the existing Social Network Services, I present a new OpenU Social Network Service based on Web 2.0 concepts. This is a next generation internet platform which can be communicated with real-time chatting and share d data and talked during seeing data by collaboration functions OpenU's main characteristics and functions by screen design and implementations are explained. And also OpenU's excellence by comparing with other SNSs system is presented.