A Text Mining Approach to the Analysis of BTS Fever

K-POP is steadily growing with global competitiveness.The rise of K-POP\u27s popularity has continued to create Korean idol groups. However, many idol groups weredismantled and there islack of measures for overseas advance and success. Therefore, this study aims to analyze the success factors of BTS by focusing onthe text mining techniques. After collecting Twitter\u27s online postings using crawling technique, we will analyzein three text mining techniques: topic modeling, keyword extraction, andterm frequencyanalysis. By analyzing data with three text miningmethods, we willderivehow BTS couldsuccess globallyand form a huge fandom. And with the derived key factors, we will suggest a success strategy based on the analysis results. In contrast to previous studies that were centered on case studiesorinterview, this study has implicationsin that the actual data was collected and analyzed through three text mining techniques

EX POST AND EX ANTE IMPACT ANALYSIS OF RESEARCH PROGRAMMES USING CMMI FRAMEWORK

In this paper, we propose a new approach for evaluating research projects and programs. According our approach, the improvement might be achieved by adopting a results-based and a project portfolio approach, and assuring a research and technology development (RTD) indicators documentation through a standard and comprehensive indicator description, named indicator template. The results-based approach will assure a consistent indicators structure, according to the results chains and a strong connection between ex ante and ex post impact evaluation. The project portfolio approach will assure a tight integration of the research performance indicators, especially between policies goals and program results. And, finally defining a comprehensive indicator template it will be possible to understand better the indicators, develop a detailed analysis, based on the business intelligence techniques, such as OLAP (On-Line Analytical Processing), data mining and text mining. According our knowledge the usage of this kind of techniques on RTD metadata is an innovative process. What we expect to find out is the indicators similarities and differentiations, the indicators clusters, the association between indicators, the most important input factors of indicators definition. According the results-based and project portfolio approach the discovered patterns will be evaluatedRDT indicator, RDT statistics, indicator template, data mining, text mining.

A Study Of Factors Contributing To Self-reported Anomalies In Civil Aviation

A study investigating what factors are present leading to pilots submitting voluntary anomaly reports regarding their flight performance was conducted. The study employed statistical methods, text mining, clustering, and dimensional reduction techniques in an effort to determine relationships between factors and anomalies. A review of the literature was conducted to determine what factors are contributing to these anomalous incidents, as well as what research exists on human error, its causes, and its management. Data from the NASA Aviation Safety Reporting System (ASRS) was analyzed using traditional statistical methods such as frequencies and multinomial logistic regression. Recently formalized approaches in text mining such as Knowledge Based Discovery (KBD) and Literature Based Discovery (LBD) were employed to create associations between factors and anomalies. These methods were also used to generate predictive models. Finally, advances in dimensional reduction techniques identified concepts or keywords within records, thus creating a framework for an unsupervised document classification system. Findings from this study reinforced established views on contributing factors to civil aviation anomalies. New associations between previously unrelated factors and conditions were also found. Dimensionality reduction also demonstrated the possibility of identifying salient factors from unstructured text records, and was able to classify these records using these identified features

The art of persuasion: An integrated text mining model for crowdfunding analysis

Crowdfunding is a substantial new funding source and growing in popularity, providing a vital service to individuals especially in developing countries, who could not otherwise get the financial resources they need. Understanding factors that determine the funders’ decisions is important in writing persuasive crowdfunding pitches for successful projects. In this paper, we present an integrated text-mining model that combines the bag-of-words model and related variables for predicting the success of crowdfunding projects in Kiva. Furthermore, association rule mining is used to identify factors that influence the funders’ decisions for writing persuasive crowdfunding pitches for successful projects

Post-mining potentials and redevelopment of former mining regions in Central Europe – Case studies from Germany and Slovenia

This article discusses the character of post-mining potentials and their role in regional development in a German and Slovenian mining region. The many possible uses often include renewable energies (biomass, geothermal energy), or tourism (museums). Discussing two case study regions, this article presents similarities and differences in approaches towards the utilisation of potentials, and compares factors that influence utilisation with reference to national framework conditions. The text argues that in the context of structural change and mine closures, the use of post-mining potentials, such as post-mining landscapes, infrastructures and traditions, can be a way to explore new development options for affected regions

Beyond ‘the Beamer, the boat and the bach’? A content analysis-based case study of New Zealand innovative firms

In this paper we will use case studies to seek to understand the dynamic innovation processes at the level of the firm and to explain the apparent 'enigma' between New Zealand's recent innovation performance and economic growth. A text-mining tool, Leximancer, (version 4) was used to analyse the case results, based on content analysis. The case studies reveal that innovation in New Zealand firms can be best described as 'internalised', and the four key factors that affect innovation in New Zealand firms are ‘Product’, ‘Market’, ‘People’ and ‘Money’. New Zealand may be an ideal place for promoting local entrepreneurship, however, many market/technology opportunities cannot be realized in such a small and isolated economy, hence the poor economic performance

Three Essays on Trust Mining in Online Social Networks

This dissertation research consists of three essays on studying trust in online social networks. Trust plays a critical role in online social relationships, because of the high levels of risk and uncertainty involved. Guided by relevant social science and computational graph theories, I develop conceptual and predictive models to gain insights into trusting behaviors in online social relationships. In the first essay, I propose a conceptual model of trust formation in online social networks. This is the first study that integrates the existing graph-based view of trust formation in social networks with socio-psychological theories of trust to provide a richer understanding of trusting behaviors in online social networks. I introduce new behavioral antecedents of trusting behaviors and redefine and integrate existing graph-based concepts to develop the proposed conceptual model. The empirical findings indicate that both socio-psychological and graph-based trust-related factors should be considered in studying trust formation in online social networks. In the second essay, I propose a theory-based predictive model to predict trust and distrust links in online social networks. Previous trust prediction models used limited network structural data to predict future trust/distrust relationships, ignoring the underlying behavioral trust-inducing factors. I identify a comprehensive set of behavioral and structural predictors of trust/distrust links based on related theories, and then build multiple supervised classification models to predict trust/distrust links in online social networks. The empirical results confirm the superior fit and predictive performance of the proposed model over the baselines. In the third essay, I propose a lexicon-based text mining model to mine trust related user-generated content (UGC). This is the first theory-based text mining model to examine important factors in online trusting decisions from UGC. I build domain-specific trustworthiness lexicons for online social networks based on related behavioral foundations and text mining techniques. Next, I propose a lexicon-based text mining model that automatically extracts and classifies trustworthiness characteristics from trust reviews. The empirical evaluations show the superior performance of the proposed text mining system over the baselines