Different Data Mining Approaches Based Medical Text Data

The amount of medical text data is increasing dramatically. Medical text data record the progress of medicine and imply a large amount of medical knowledge. As a natural language, they are characterized by semistructured, high-dimensional, high data volume semantics and cannot participate in arithmetic operations. Therefore, how to extract useful knowledge or information from the total available data is very important task. Using various techniques of data mining can extract valuable knowledge or information from data. In the current study, we reviewed different approaches to apply for medical text data mining. The advantages and shortcomings for each technique compared to different processes of medical text data were analyzed. We also explored the applications of algorithms for providing insights to the users and enabling them to use the resources for the specific challenges in medical text data. Further, the main challenges in medical text data mining were discussed. Findings of this paper are benefit for helping the researchers to choose the reasonable techniques for mining medical text data and presenting the main challenges to them in medical text data mining.

Text Data Mining Algorithm Combining CNN and DBM Models

The special text has a lot of features, such as professional words, abbreviations, large datasets, different themes, and uneven distribution of labels. While the existing text data mining classification methods use simple machine learning models, it has a bad performance on text classification. To solve this drawback, a text data mining algorithm based on convolutional neural network (CNN) model and deep Boltzmann machines (DBM) model is proposed in this paper. This method combines the CNN and DBM models with good feature extraction to realize the double feature extraction. It can realize the tag tree by constructing the tag tree and design the effective hierarchical network to achieve classification. At the same time, the model can suppress the input noise on the classification. Experimental results show that the improved algorithm achieves good classification results in special text data mining.

Corporate social responsibility in international business literature: results from text data mining of the Journal of International Business Studies

Corporate social responsibility has been an important theme in management at least since the 1960s. International business became a recognized subfield in management around the same time. Logically, there might have been much dialogue about corporate social responsibility in international business research and publication, yet previous reviews of the literature indicate relatively little such research. This study complements previous literature reviews by employing text data mining to analyze a sample of 1188 articles published from 2000 to 2018 in the Journal of International Business Studies (JIBS). Results show that from 2000 to 2018 only 35 CSR focused articles appeared. CSR research has increased over time, highly influenced by editorial specification of special issues. These documents can be grouped into seven CSR topics, with corruption and embeddedness being the most salient. Strategies are suggested for increasing research on CSR in international business.

Democracy Education for Children using a Cartoon Video and Mock Voting

In democracy education, determining how best to teach young children about democracy and how to measure the effectiveness of such learning is difficult, as "democracy" is a subjective and intangible concept. Given the challenge that this presents to educational planners, the author has created a cartoon video about democracy accompanied by an opportunity for children who watch the video to "mock vote." The author used the video in a series of elementary school visits to teach the children the meaning of democracy. The effects of the video learning were assessed by analyzing the children's questionnaire responses before and after the class using text data mining. It was found that the children were able to assimilate the contents of the video and the themes behind the story; that is, they understood the meaning of democracy and democratic elections.