Topic Modeling of Marketing Scientific Papers: An Experimental Survey

2021 ◽  
pp. 147-171
Author(s):  
Malek Chebil ◽  
Rim Jallouli ◽  
Mohamed Anis Bach Tobji ◽  
Chiheb Eddine Ben Ncir
Keyword(s):  
Topic Modeling ◽  
Scientific Papers ◽  
Experimental Survey

scholarly journals Identification of Topics from Scientific Papers through Topic Modeling

2021 ◽  
Vol 10 (04) ◽  
pp. 541-548
Author(s):  
Denis Luiz Marcello Owa
Keyword(s):  
Topic Modeling ◽  
Scientific Papers

scholarly journals Emerging Research Topic Detection Using Filtered-LDA

AI ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 578-599
Author(s):  
Fuad Alattar ◽  
Khaled Shaalan
Keyword(s):  
Final Stage ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Research Topic ◽  
Main Topic ◽  
Topic Detection ◽  
Scientific Papers ◽  
Trending Topics ◽  
Emerging Topics ◽  
Dirichlet Allocation

Comparing two sets of documents to identify new topics is useful in many applications, like discovering trending topics from sets of scientific papers, emerging topic detection in microblogs, and interpreting sentiment variations in Twitter. In this paper, the main topic-modeling-based approaches to address this task are examined to identify limitations and necessary enhancements. To overcome these limitations, we introduce two separate frameworks to discover emerging topics through a filtered latent Dirichlet allocation (filtered-LDA) model. The model acts as a filter that identifies old topics from a timestamped set of documents, removes all documents that focus on old topics, and keeps documents that discuss new topics. Filtered-LDA also genuinely reduces the chance of using keywords from old topics to represent emerging topics. The final stage of the filter uses multiple topic visualization formats to improve human interpretability of the filtered topics, and it presents the most-representative document for each topic.

scholarly journals Layer-Assisted Neural Topic Modeling over Document Networks

2021 ◽  
Author(s):  
Yiming Wang ◽  
Ximing Li ◽  
Jihong Ouyang
Keyword(s):  
Text Classification ◽  
Topic Modeling ◽  
Link Prediction ◽  
Topic Model ◽  
Web Pages ◽  
Text Documents ◽  
Text Data ◽  
Generative Process ◽  
Network Links ◽  
Scientific Papers

Neural topic modeling provides a flexible, efficient, and powerful way to extract topic representations from text documents. Unfortunately, most existing models cannot handle the text data with network links, such as web pages with hyperlinks and scientific papers with citations. To resolve this kind of data, we develop a novel neural topic model , namely Layer-Assisted Neural Topic Model (LANTM), which can be interpreted from the perspective of variational auto-encoders. Our major motivation is to enhance the topic representation encoding by not only using text contents, but also the assisted network links. Specifically, LANTM encodes the texts and network links to the topic representations by an augmented network with graph convolutional modules, and decodes them by maximizing the likelihood of the generative process. The neural variational inference is adopted for efficient inference. Experimental results validate that LANTM significantly outperforms the existing models on topic quality, text classification and link prediction..

Graph Convolutional Network-Strengthened Topic Modeling for Scientific Papers

2021 ◽  
Author(s):  
Jia Zhang ◽  
Junhao Shen ◽  
Beichen Hu ◽  
Rahul Ramachandran ◽  
Tsengdar J. Lee ◽  
...  
Keyword(s):  
Topic Modeling ◽  
Convolutional Network ◽  
Scientific Papers

scholarly journals Text Mining Tourism Literature

2021 ◽  
Author(s):  
Ajda Pretnar ◽  
Tomaž Curk
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Energy Efficiency ◽  
Topic Modeling ◽  
Research Focus ◽  
Specific Domain ◽  
Main Research ◽  
Online Databases ◽  
Literature Reviews ◽  
Scientific Papers

Literature reviews are essential for understanding a specific domain as they map the main topics of current re-search. Our aim was to provide a framework for retrieving articles from online databases and analyzing them in a single script. We provide the analytical pipeline as open-source (https://github.com/tourism4-0/BibMine). The main research focus was on analyzing 318 abstracts from scientific papers on tourism and innovation, which we report in Zach et al. (2019). We used LDA topic modeling to uncover ten main topics, which we analyzed using pyLDAvis visualization. We used saliency and relevance scores to determine the main words that de-scribe a topic. The uncovered topics range from climate change and land use to smart destinations, travel expe-riences, and ICT. We performed similar analyses for the term "stakeholders," where we also observed the main verbs related to the query. Since verbs best define an activity, we used them to determine how stakeholders are involved in tourism development. Finally, we analyzed papers with the keyword "technology," where energy efficiency, VR, web technology, and augmented tourist experiences were the main topics.

The Scientific Papers of Sir George Darwin

2009 ◽  
Author(s):  
George Howard Darwin
Keyword(s):  
Scientific Papers

Centenary year of scientific papers in the British Dental Journal

BDJ ◽  
1996 ◽  
Vol 181 (6) ◽  
pp. 226-227
Author(s):  
D McGowan
Keyword(s):  
Scientific Papers
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