Investigations on Techniques and Applications of Text Analytics

Abstract

The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs.Among the various types of unstructured data, text data have gained particular attention because it is the most representative method to describe and deliver information. Text analysis is generally performed in the following order: document collection, parsing and filtering, structuring, frequency analysis, and similarity analysis. The results of the analysis can be displayed through word cloud, word network, topic modeling, document classification, and semantic analysis. Notably, there is an increasing demand to identify trending topics from the rapidly increasing text data generated through various social media. Thus, research on and applications of topic modeling have been actively carried out in various fields since topic modeling is able to extract the core topics from a huge amount of unstructured text documents and provide the document groups for each different topic. In this paper, we review the major techniques and research trends of text analysis. Further, we also introduce some cases of applications that solve the problems in various fields by using topic modeling.

Development of Mission and Vision of College of Korean Medicine Using the Delphi Techniques and Big-Data Analysis

“…Word Cloud is an analysis technique that visualizes the number of keywords based on frequency analysis [13]. The method is the were specifically chosen because this allowed for a significant distinction.…”

Analysis of research on metabolic syndrome in cancer survivors using topic modeling and social network analysis

“…35 It also calculates the probability of each document and word being included in a specific topic and that of each word obtained from the entire document being included in a specific topic. 36 In other words, topic modeling analysis assumes that each article is a collection of arbitrary issues and probabilistically presents the importance of issues and words comprising each article. 37 In this study, core issues were identified in the collected abstracts and labeled, and changes in issues over time were examined.…”

“…Setting a proper number of topics is critical, as the results of topic modeling can vary according to the number of previously set topics and proper methods for searching the number of topics studied. 36 Based on a study reporting that a proper number of topics depend on good categorization as determined by researchers, 38 several topics were determined to be well-categorized from the replication analysis in this study. Consequently, using topic modeling by applying the Gibbs sampling method for the LDA analysis, 39 five topics were extracted in this study, with the topic modeling parameters set as α = 0.1 and β = 0.01 according to Zhao et al 39 The search period for 2019 and the abstracts of articles published until February 2019 were excluded from this study, as these could have affected the trend in topics.…”

Trend Research of Maritime Autonomous Surface Ships (MASS) based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

“…The findings reveal that articles on online media and the sharing economy receive high citations, whereas recently published articles on specific topics such as rural tourism and ecotourism are frequently cited. This study contributes significantly to the hospitality and tourism literature by examining the impact of topic structure and originality, identified through text mining, on citation counts [28].…”

“…A network in co-occurrence network analysis comprises nodes and connections (links) between nodes, with each node representing a keyword and each link representing a relationship between keywords. To visually represent the relationship between keywords, each keyword is represented by a dot, and the link between keywords is represented by a line, and the thickness of the line varies depending on how often the two keywords are used together [38]. If a network analysis is conducted for all keywords, it becomes difficult to grasp the meaning due to too many nodes and connections, hence the range of nodes to be included in the analysis must be determined [39].…”