• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.15-28
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• 2022

Due to recent expansion of online market such as clothing, utilizing customer review has become a major marketing measure. User review has been used as a tool of analyzing sentiment of customers. Sentiment analysis can be largely classified with machine learning-based and lexicon-based method. Machine learning-based method is a learning classification model referring review and labels. As research of sentiment analysis has been developed, multi-modal models learned by images and video data in reviews has been studied. Characteristics of words in reviews are differentiated depending on products' and customers' categories. In this paper, sentiment is analyzed via considering review data and metadata of products and users. Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Self Attention-based Multi-head Attention models and Bidirectional Encoder Representation from Transformer (BERT) are used in this study. Same Multi-Layer Perceptron (MLP) model is used upon every products information. This paper suggests a multi-modal sentiment analysis model that simultaneously considers user reviews and product meta-information.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.239-251
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• 2010

In modern distributed enterprise applications that have multilayered architecture, business entities are a kind of crosscutting concerns running through service components that implements business logic in each layer. When business entities are modified, service components related to them should also be modified so that they can deal with those business entities with new types, even though their functionality remains the same. Our previous paper proposed what we call the DTT (Data Type-Tolerant) component model to efficiently process the variability of business entities, which are data externalized from service components. While the DTT component model, by removing direct coupling between service components and business entities, exempts the need to rewrite service components when business entities are modified, it incurs the burden of implementing data type converters that mediate between them. To solve this problem, this paper proposes a method to use ontology as the metadata of both SCDTs (Self-Contained Data Types) in service components and business entities, and a method to generate data type converter code using the ontology. This ontology-based DTT component model greatly enhances the reusability of service components and the efficiency in processing data variability by allowing the computer to automatically generate data type converters without error.

• Journal of KIISE:Software and Applications
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• v.29 no.7
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• pp.476-486
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• 2002

CBD(Component Based Development) has become the best strategical method for the business application. Because CBD is a new development paradigm which makes it possible to assemble the software components for application, it copes with the rapid challenge of business process and meets the increasing requirements for productivity. Since the business process is rapidly changing, CBD technology is the promising way to solve the productivity. Especially, the repository is the most important part for the development, distribution and reuse of components. In component repository, we can store and manage the related work-products produced at each step of component development as well as component itself. In this paper, we suggested a practical approach for repository construction to support and realize the CBD process and developed the CRMS(Component Repository Management System) as implementation product of the proposed techniques. CRMS can manage a variety of component products based on component architecture, and help software developers to search a candidate component for their project and to understand a variety of information for the component. In the paper, a practical approach for component repository was suggested, and a supporting environment was constructed to make CBD to be working efficiently. We expect this work wall be valuable research for component repository and the entire supporting Component Based Development Process.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.73-80
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• 2021

In this paper, we propose a packet processing method capable of handling large-capacity traffic over 20Gbps using multi-core and huge page memory approaches. As ICT technology advances, the global average monthly traffic is expected to reach 396 exabytes by 2022. With the increase in network traffic, cyber threats are also increasing, increasing the importance of traffic analysis. Traffic analyzed as an existing high-cost foreign product simply stores statistical data and visually shows it. Network administrators introduce and analyze many traffic analysis systems to analyze traffic in various sections, but they cannot check the aggregated traffic of the entire network. In addition, since most of the existing equipment is of the 10Gbps class, it cannot handle the increasing traffic every year at a fast speed. In this paper, as a method of processing large-capacity traffic over 20Gbps, the process of processing raw packets without copying from single-core and basic SMA memory approaches to high-performance packet reception, packet detection, and statistics using multi-core and NUMA memory approaches suggest When using the proposed method, it was confirmed that more than 50% of the traffic was processed compared to the existing equipment.

• Journal of the Korean Society for information Management
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• v.37 no.4
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• pp.1-26
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• 2020

In today's increasingly data-intensive academic environment, data is becoming the foundation of academic communication as a research outcome rather than a research by-product. However, there is a limit to guaranteeing actual data reuse only by expanding the data supply or securing accessibility. In order to overcome this, it is necessary to understand the data reuse behavior and data needs in-depth. Therefore, this study attempted to identify the major data reuse behavior and data needs among researchers. To this end, the authors of KCI papers among the data reuse documents of the Korea Social Science Data Archive (KOSSDA) for the past 3 years were targeted. An in-depth interview was conducted with 12 researchers who accepted the interview. As a result, factors considered when reusing data were personal, economic, technical, and social aspects, and it was found that the data itself was used or contextual information of the data was used depending on the purpose of data reuse. The path to acquiring data is a web-based source of information, and a path through informal communication can also be found. In terms of the data needs, it was found that they prefer English, the United States, and institutional producers. Also they have a clear preference for quantitative data from an interviewer-filled interpersonal interview survey method, rich metadata along with raw data, and data that contains identification information. However, due to the lack of confidence in the value, it is negative for the use of data with controlled access and use, and it is difficult to confirm a clear preference because there is no similar data available for selection in terms of size and freshness.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.614-629
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• 2001

As the information-intensive society in 21c based on the environment of global internet is promoted, the software is getting more large and complex, and the demand for the software is increasing briskly. So, it becomes an important issue in academic and industrial field to activate reuse by developing and exchanging the standardized component. Currently, the information services as a product type of each company are provided in foreign market place for reusing a commercial component, but the components which are serviced in each market place are different, insufficient and unstandardized. That is, construction for Component Data Registry based on ISO 11179, is not accomplished. Hence, the national government has stepped up the plan for sending out public component at 2001. Therefore, the systems as a tool for sharing and exchange of data, have to support the meta-information of standardized component. In this paper, we will propose the C_MDR system: a tool to register and manage the standardized meta-information, based upon ISO 11179, for the commercialized common component. The purpose of this system is to systemically share and exchange the data in chain of acceleration of reusing the component. So, we will show the platform of specification for the component meta-information, then define the meta-information according to this platform, also represent the meta-information using XML for enhancing the interoperability of information with other system. Moreover, we will show that three-layered expression make modeling to be simple and understandable. The implementation of this system is to construct a prototype system of the component meta-information through the internet on www, this system uses ASP as a development language and RDBMS Oracle for PC. Thus, we may expect the standardization of the exchanged component metadata, and be able to apply to the exchanged reuse tool.