Social Semantics and Similarities from User-generated Keywords to Information Retrieval: A Case Study of Social Tags in Marine Science

Abstract

Of late, social tagging has become popular trend in information organisation. In context of digital resources the tags assigned by users also play vital role in information retrieval. For information discovery the ‘terms’ used to retrieve the results also depend upon the ‘relevancy’ or ‘weightage’ of the keywords. This study investigates ‘relevancy ranking’ of terms used in the full text of the resource. The common words present in both full text of the article and social tags were considered for the study by employing TF-IDF statistical technique and Jaccard similarity test. The results show that it is possible to assign ‘weight’ to keywords for better results and also determine the significant tags assigned by the user. The Jaccard similarity coefficient test adopted to understand the word similarity between full text words of an article and marine social tags. This work reveals the social tags can enrich metadata for information retrieval.

Analysis of The Development Trend of Sports Research in China and Taiwan Using Natural Language Processing

“…The process of this study was based on the concepts of text mining and NLP. For more, please refer to [5][6][7][14][15][16]18,[27][28][29][30]. In the SCI (science citation index) and SSCI (social science citation index) articles of [31], through the steps of text retrieval, word segmentation, word cloud analysis, TF-IDF analysis, co-word analysis, network analysis, trend analysis, etc., the text data of the two regions are excavated for comparison and analysis of their similarities and differences.…”

“…It can convert words into statistical data and calculate their frequency. TF indicates the number of times words appear in the text, while IDF can show the importance of words in all texts, and at the same time increase common words and expand rare words [5,18]. Therefore, if key words appear in more different texts and have a lower frequency in each text, they can obtain more weight.…”

“…Therefore, if key words appear in more different texts and have a lower frequency in each text, they can obtain more weight. Refer to the explanations of TF-IDF conversion by [32] and [18]. In the general file (F) of all samples, taking the words (wi) of all words (Dterms) in a file (f1) as an example, the formula is as follows:…”

“…In order to make up for the shortcomings of the content analysis method, such as being time-consuming, laborious, and not objective, this study proposes a program for analyzing and processing text data in sports-related fields based on the concept of NLP, which is different from the traditional content analysis method, with a faster automative analytical process and the ability to systematically extract and analyze the importance and relevance of keywords in abstract content [5,13]. It integrates the methods of word segmentation, word cloud analysis, TF-IDF (term frequency-inverse document frequency) analysis, and co-word analysis, and then applies visual network diagramics drawing technology [14][15][16][17][18] to present the distribution of core keywords and their network relationships in sports research abstracts from China and Taiwan (a total of 3,283 articles). This reveals possible hidden knowledge structures and provides an important reference for evaluating future development trends in the two regions.…”

“…Text mining is much more complicated, because its original input data are unstructured information in the form of text, unlike structured data, which have their specific structure to follow; however, in these seemingly messy text data, there is also a lot of information worthy of in-depth investigation, which needs to be explored by relevant researchers [26]. Therefore, NLP applies the language rules commonly used by people to carry out disassembly and reorganization operations for the words in the text content, i.e., it can grasp the importance and relevance of keywords, and thus can roughly understand the semantic content and become an important basis for exploring new knowledge [5,18].…”