Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userHow the Taiwanese Do China Studies: Applications of Text Mining
62
0
0.0
(
0
)
Added by
Yun-Cheng Tsai
Publication date
2018
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
With the rapid evolution of cross-strait situation, Mainland China as a subject of social science study has evoked the voice of Rethinking China Study among intelligentsia recently. This essay tried to apply an automatic content analysis tool (CATAR) to the journal Mainland China Studies (1998-2015) in order to observe the research trends based on the clustering of text from the title and abstract of each paper in the journal. The results showed that the 473 articles published by the journal were clustered into seven salient topics. From the publication number of each topic over time (including volume of publications, percentage of publications), there are two major topics of this journal while other topics varied over time widely. The contribution of this study includes: 1. We could group each independent study into a meaningful topic, as a small scale experiment verified that this topic clustering is feasible. 2. This essay reveals the salient research topics and their trends for the Taiwan journal Mainland China Studies. 3. Various topical keywords were identified, providing easy access to the past study. 4. The yearly trends of the identified topics could be viewed as signature of future research directions.
rate research
Read More
Chinas scientific output has risen precipitously over the past decade; it is now the worlds second-largest producer of scientific papers, behind only the United States. The quality of Chinas research is also on the rise (Van Noorden, 2016). The online visibility and impact of Chinas research are also important issues worth exploring. In this study, we investigate the altmetric performance of publications in the field of Biotechnology and Applied Microbiology and published by authors from Chinese affiliations. We find that papers published by those authors from Chinese affiliations have much lower visibility on the social web than articles from other countries, when there is no significant difference for the citations. Fewer of Chinas publications get tweeted, and those tweeted publications attract less social attention. A geographical analysis of tweeters shows that scholarly articles get most of their social attention from the authors home countries, a finding that is also confirmed by correlation and regression analysis. This situation, which is unfavorable for researchers from Chinese affiliations, is caused, in part, by the inaccessibility of mainstream social networking platforms in mainland China.
This paper describes an efficiently scalable approach to measure technological similarity between patents by combining embedding techniques from natural language processing with nearest-neighbor approximation. Using this methodology we are able to compute existing similarities between all patents, which in turn enables us to represent the whole patent universe as a technological network. We validate both technological signature and similarity in various ways, and demonstrate at the case of electric vehicle technologies their usefulness to measure knowledge flows, map technological change, and create patent quality indicators. Thereby the paper contributes to the growing literature on text-based indicators for patent landscaping.
In contrast to other fields where conferences are typically for less polished or in-progress research, computing has long relied on referred conference papers as a venue for the final publication of completed research. While frequently a topic of informal discussion, debates about its efficacy, or library science research, the development of this phenomena has not been historically analyzed. This paper presents the first systematic investigation of the development of modern computing publications. It relies on semi-structured interviews with eight computing professors from diverse backgrounds to understand how researchers experienced changes in publication culture over time. Ultimately, the article concludes that the early presence of non-academic practitioners in research and a degree of path dependenceor a tendency to continue on the established path rather than the most economically optimal one allowed conferences to gain and hold prominence as the field exploded in popularity during the 1980s.
Research on the construction of traditional information science methodology taxonomy is mostly conducted manually. From the limited corpus, researchers have attempted to summarize some of the research methodology entities into several abstract levels (generally three levels); however, they have been unable to provide a more granular hierarchy. Moreover, updating the methodology taxonomy is traditionally a slow process. In this study, we collected full-text academic papers related to information science. First, we constructed a basic methodology taxonomy with three levels by manual annotation. Then, the word vectors of the research methodology entities were trained using the full-text data. Accordingly, the research methodology entities were clustered and the basic methodology taxonomy was expanded using the clustering results to obtain a methodology taxonomy with more levels. This study provides new concepts for constructing a methodology taxonomy of information science. The proposed methodology taxonomy is semi-automated; it is more detailed than conventional schemes and the speed of taxonomy renewal has been enhanced.
Motivated by recent interest in the status and consequences of competition between the U.S. and China in A.I. research, we analyze 60 years of abstract data scraped from Scopus to explore and quantify trends in publications on A.I. topics from institutions affiliated with each country. We find the total volume of publications produced in both countries grows with a remarkable regularity over tens of years. While China initially experienced faster growth in publication volume than the U.S., growth slowed in China when it reached parity with the U.S. and the growth rates of both countries are now similar. We also see both countries undergo a seismic shift in topic choice around 1990, and connect this to an explosion of interest in neural network methods. Finally, we see evidence that between 2000 and 2010, Chinas topic choice tended to lag that of the U.S. but that in recent decades the topic portfolios have come into closer alignment.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
