Design and evaluation of a parallel document clustering algorithm based on hierarchical latent semantic analysis

2018 ◽  
Vol 31 (13) ◽  
pp. e5094 ◽  
Author(s):  
Karthick Seshadri ◽  
K. Viswanathan Iyer ◽  
Mercy Shalinie S
Keyword(s):  
Latent Semantic Analysis ◽  
Clustering Algorithm ◽  
Semantic Analysis ◽  
Document Clustering

scholarly journals Automatic assessment of online discussions using text mining

2012 ◽  
Vol 1 (1) ◽  
Author(s):  
Yvette Awuor ◽  
Robert Oboko
Keyword(s):  
Latent Semantic Analysis ◽  
Clustering Algorithm ◽  
Semantic Analysis ◽  
Online Discussion ◽  
Online Discussions ◽  
Discussion Forums ◽  
Automatic Assessment ◽  
Online Discussion Forums ◽  
Management Forum ◽  
Heavy Burden

Online discussion forums have rapidly gained usage in e-learning systems. This has placed a heavy burden on course instructors in terms of moderating student discussions. Previous methods of assessing student participation in online discussions followed strictly quantitative approaches that did not necessarily capture the students’ effort. Along with this growth in usage there is a need for accelerated knowledge extraction tools for analysing and presenting online messages in a useful and meaningful manner. This article discussed a qualitative approach which involves content analysis of the discussions and generation of clustered keywords which can be used to identify topics of discussion. The authors applied a new k-means++ clustering algorithm with latent semantic analysis to assess the topics expressed by students in online discussion forums. The proposed algorithm was then compared with the standard k-means++ algorithm. Using the Moodle course management forum to validate the proposed algorithm, the authors show that the k-mean++ clustering algorithm with latent semantic analysis performs better than a stand-alone k-means++.

scholarly journals Bert-Based Latent Semantic Analysis (Bert-LSA): A Case Study on Geospatial Data Technology and Application Trend Analysis

2021 ◽  
Vol 11 (24) ◽  
pp. 11897
Author(s):  
Quanying Cheng ◽  
Yunqiang Zhu ◽  
Jia Song ◽  
Hongyun Zeng ◽  
Shu Wang ◽  
...  
Keyword(s):  
Trend Analysis ◽  
Latent Semantic Analysis ◽  
Clustering Algorithm ◽  
Semantic Analysis ◽  
Relevant Literature ◽  
Geospatial Data ◽  
Probabilistic Latent Semantic Analysis ◽  
Data Resource ◽  
Topic Analysis ◽  
Text Content

Geospatial data is an indispensable data resource for research and applications in many fields. The technologies and applications related to geospatial data are constantly advancing and updating, so identifying the technologies and applications among them will help foster and fund further innovation. Through topic analysis, new research hotspots can be discovered by understanding the whole development process of a topic. At present, the main methods to determine topics are peer review and bibliometrics, however they just review relevant literature or perform simple frequency analysis. This paper proposes a new topic discovery method, which combines a word embedding method, based on a pre-trained model, Bert, and a spherical k-means clustering algorithm, and applies the similarity between literature and topics to assign literature to different topics. The proposed method was applied to 266 pieces of literature related to geospatial data over the past five years. First, according to the number of publications, the trend analysis of technologies and applications related to geospatial data in several leading countries was conducted. Then, the consistency of the proposed method and the existing method PLSA (Probabilistic Latent Semantic Analysis) was evaluated by using two similar consistency evaluation indicators (i.e., U-Mass and NMPI). The results show that the method proposed in this paper can well reveal text content, determine development trends, and produce more coherent topics, and that the overall performance of Bert-LSA is better than PLSA using NPMI and U-Mass. This method is not limited to trend analysis using the data in this paper; it can also be used for the topic analysis of other types of texts.

Task Estimation Using Latent Semantic Analysis of Visual Scenes and Spoken Words

2012 ◽  
Vol 132 (9) ◽  
pp. 1473-1480
Author(s):  
Masashi Kimura ◽  
Shinta Sawada ◽  
Yurie Iribe ◽  
Kouichi Katsurada ◽  
Tsuneo Nitta
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Spoken Words ◽  
Visual Scenes
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