More than 1.5 million academic documents are published each year, and this trend shows an incremental tendency for the following years. One of the main challenges for the academic community is how to organize this huge volume of documentation to have a sense of the knowledge frontier. In this study we applied Latent Dirichlet Allocation (LDA) techniques to identify primary topics in organization studies, and analyzed the relationships between academic impact and belonging to the topics detected by LDA
Topic modeling has been used widely to extract the structures (topics) in a collection (corpus) of d...
Research data management (RDM) is often seen as the overarching field that permits research data to ...
This study presents a method to analyze textual data and applying it to the field of Library and Inf...
Unsupervised statistical analysis of unstructured data has gained wide acceptance especially in natu...
This study investigated the evolution of library and information science (LIS) by analyzing researc...
As the information load grows, it becomes increasingly difficult to follow-up new trends in business...
Latent Dirichlet Allocation (LDA) is a popular machine-learning technique that identifies latent str...
This work aims at discovering topics in a text corpus and classifying the most relevant terms for ea...
Before conducting a research project, researchers must find the trends and state of the art in their...
Before conducting a research project, researchers must find the trends and state of the art in their...
Before conducting a research project, researchers must find the trends and state of the art in their...
Journal is a scholarly publication that are usually published by researchers and discuss about the c...
This work presents our attempt to understand the research topics that characterize the papers submit...
Topic modeling has been used widely to extract the structures (topics) in a collection (corpus) of d...
Topic modeling has been used widely to extract the structures (topics) in a collection (corpus) of d...
Topic modeling has been used widely to extract the structures (topics) in a collection (corpus) of d...
Research data management (RDM) is often seen as the overarching field that permits research data to ...
This study presents a method to analyze textual data and applying it to the field of Library and Inf...
Unsupervised statistical analysis of unstructured data has gained wide acceptance especially in natu...
This study investigated the evolution of library and information science (LIS) by analyzing researc...
As the information load grows, it becomes increasingly difficult to follow-up new trends in business...
Latent Dirichlet Allocation (LDA) is a popular machine-learning technique that identifies latent str...
This work aims at discovering topics in a text corpus and classifying the most relevant terms for ea...
Before conducting a research project, researchers must find the trends and state of the art in their...
Before conducting a research project, researchers must find the trends and state of the art in their...
Before conducting a research project, researchers must find the trends and state of the art in their...
Journal is a scholarly publication that are usually published by researchers and discuss about the c...
This work presents our attempt to understand the research topics that characterize the papers submit...
Topic modeling has been used widely to extract the structures (topics) in a collection (corpus) of d...
Topic modeling has been used widely to extract the structures (topics) in a collection (corpus) of d...
Topic modeling has been used widely to extract the structures (topics) in a collection (corpus) of d...
Research data management (RDM) is often seen as the overarching field that permits research data to ...
This study presents a method to analyze textual data and applying it to the field of Library and Inf...