An Investigation for the Research Trends in International Library and Information Science with a Focus on Textual Terms
| Autor: | Chih-Chun Kang, 康秩群 |
|---|---|
| Rok vydání: | 2017 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 105 This study aims to investigate the research trends in the field of library and information science (LIS) from 2000 to 2016, leveraging an automatic method for processing a large amount of data. This study includes two parts: Part I: Constructing a web application that counts the terms and its combinations in the input data set, extracts the terms with high document frequency to build up stop word lists, and then show the results by visual representations. Part II: We then use the tool constructed in part I, with recent articles in LIS field as input data, observe and analyze the research trends. Finally, results are compared with former research. The stop word lists are constructed in accordance with the document frequency of the terms, then function words, common words in academic writing, and common words in LIS field, such as “information” and “system”, are extracted. As for the trends of terms in the observed period, the terms related to social network and data processing have ascending trends, whereas the terms related to search engine, web, (digital) library and information seeking behavior have descending trends. The trends of popular research topics from 2000 began with information retrieval system, information behavior, and web research, followed by search engine and citation analysis. Citation analysis dominates the LIS field from 2005 to 2014. Social network sprang up around 2010 and became the mainstream of LIS researches nowadays. Comparing with the results of keyword analysis by Chang et al.(2015), we can see that they are basically similar. However, the method in this study can extract more things that are regard as research tools or objects, and the keyword analysis has the advantage of identifying abstract topics. Since we took TF-IDF model as the core idea of weighting the terms, the terms that only occur in few articles—even only one article—might have unproportionate weight, and become noise in the results. The terms, which have high term frequency but only occur in few articles, should not have such high weights in the application of observing the overall trends. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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