Topic Structure Mining for Document Sets Using Graph-Based Analysis

2006 ◽  
pp. 327-337 ◽  
Author(s):  
Hiroyuki Toda ◽  
Ryoji Kataoka ◽  
Hiroyuki Kitagawa
Keyword(s):  
Topic Structure

A topic modeling analysis of Korea’s T&I research trends in the 2010s

Babel ◽  
2021 ◽  
Author(s):  
Changsoo Lee
Keyword(s):  
Topic Modeling ◽  
Research Output ◽  
Research Trends ◽  
Korean Language ◽  
Modeling Analysis ◽  
Trends Over Time ◽  
New Research ◽  
Topic Structure ◽  
Manual Classification

Abstract The present study aims to demonstrate the relevance of topic modeling as a new research tool for analyzing research trends in the T&I field. Until now, most efforts to this end have relied on manual classification based on pre-established typologies. This method is time- and labor-consuming, prone to subjective biases, and limited in describing a vast amount of research output. As a key component of text mining, topic modeling offers an efficient way of summarizing topic structure and trends over time in a collection of documents while being able to describe the entire system without having to rely on sampling. As a case study, the present paper applies the technique to analyzing a collection of abstracts from four Korean Language T&I journals for the 2010s decade (from 2010 to 2019). The analysis proves the technique to be highly successful in uncovering hidden topical structure and trends in the abstract corpus. The results are discussed along with implications of the technique for the T&I field.

scholarly journals What Are the Drivers of Citations?: Application in Tourism and Hospitality Journals

2021 ◽  
Vol 11 (19) ◽  
pp. 9288
Author(s):  
Eunhye Park ◽  
Woohyuk Kim
Keyword(s):  
Topic Modeling ◽  
Web Of Science ◽  
Sharing Economy ◽  
Online Media ◽  
Research Topics ◽  
Qualitative And Quantitative ◽  
Hospitality And Tourism ◽  
Citation Counts ◽  
Tourism And Hospitality ◽  
Topic Structure

In line with the qualitative and quantitative growth of academic papers, it is critical to understand the factors driving citations in scholarly articles. This study discovered the up-to-date academic structure in the tourism and hospitality literature and tested the comprehensive sets of factors driving citation counts using articles published in first-tier hospitality and tourism journals found on the Web of Science. To further test the effects of research topic structure on citation counts, unsupervised topic modeling was conducted with 9910 tourism and hospitality papers published in 12 journals over 10 years. Articles specific to online media and the sharing economy have received numerous citations and that recently published papers with particular research topics (e.g., rural tourism and eco-tourism) were frequently cited. This study makes a major contribution to hospitality and tourism literature by testing the effects of topic structure and topic originality discovered by text mining on citation counts.

scholarly journals Signaling topic structure via headings or preview sentences

2013 ◽  
Vol 19 (2) ◽  
pp. 59-66 ◽  
Author(s):  
Robert F. Lorch ◽  
Julie Lemarié ◽  
Hung-Tao Chen
Keyword(s):  
Topic Structure

scholarly journals Topic Structure Affects Semantic Integration: Evidence from Event-Related Potentials

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e79734 ◽  
Author(s):  
Xiaohong Yang ◽  
Xuhai Chen ◽  
Shuang Chen ◽  
Xiaoying Xu ◽  
Yufang Yang
Keyword(s):  
Event Related Potentials ◽  
Semantic Integration ◽  
Related Potentials ◽  
Topic Structure

Task effects and individual differences in on‐line processing of the topic structure of a text

1987 ◽  
Vol 10 (1) ◽  
pp. 63-80 ◽  
Author(s):  
Robert F. Lorch ◽  
Elizabeth Pugzles Lorch ◽  
Ann M. Mogan
Keyword(s):  
Individual Differences ◽  
Task Effects ◽  
On Line ◽  
Topic Structure

An ensemble clustering approach for topic discovery using implicit text segmentation

2020 ◽  
pp. 016555152091159
Author(s):  
Muhammad Qasim Memon ◽  
Yu Lu ◽  
Penghe Chen ◽  
Aasma Memon ◽  
Muhammad Salman Pathan ◽  
...  
Keyword(s):  
Clustering Algorithms ◽  
Data Retrieval ◽  
Text Clustering ◽  
Text Segmentation ◽  
Ensemble Clustering ◽  
Reduction Techniques ◽  
Average Improvement ◽  
Text Collections ◽  
Clustering Approach ◽  
Topic Structure

Text segmentation (TS) is the process of dividing multi-topic text collections into cohesive segments using topic boundaries. Similarly, text clustering has been renowned as a major concern when it comes to multi-topic text collections, as they are distinguished by sub-topic structure and their contents are not associated with each other. Existing clustering approaches follow the TS method which relies on word frequencies and may not be suitable to cluster multi-topic text collections. In this work, we propose a new ensemble clustering approach (ECA) is a novel topic-modelling-based clustering approach, which induces the combination of TS and text clustering. We improvised a LDA-onto (LDA-ontology) is a TS-based model, which presents a deterioration of a document into segments (i.e. sub-documents), wherein each sub-document is associated with exactly one sub-topic. We deal with the problem of clustering when it comes to a document that is intrinsically related to various topics and its topical structure is missing. ECA is tested through well-known datasets in order to provide a comprehensive presentation and validation of clustering algorithms using LDA-onto. ECA exhibits the semantic relations of keywords in sub-documents and resultant clusters belong to original documents that they contain. Moreover, present research sheds the light on clustering performances and it indicates that there is no difference over performances (in terms of F-measure) when the number of topics changes. Our findings give above par results in order to analyse the problem of text clustering in a broader spectrum without applying dimension reduction techniques over high sparse data. Specifically, ECA provides an efficient and significant framework than the traditional and segment-based approach, such that achieved results are statistically significant with an average improvement of over 10.2%. For the most part, proposed framework can be evaluated in applications where meaningful data retrieval is useful, such as document summarization, text retrieval, novelty and topic detection.

scholarly journals iPoster: Interactive Poster Generation based on　Topic Structure and Slide Presentation

2015 ◽  
Vol 30 (1) ◽  
pp. 112-123 ◽  
Author(s):  
Yuanyuan Wang ◽  
Yukiko Kawai ◽  
Kazutoshi Sumiya
Keyword(s):  
Slide Presentation ◽  
Topic Structure
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