Agreeing to Disagree: Choosing Among Eight Topic-Modeling Methods

2021 ◽  
Vol 23 ◽  
pp. 100173
Author(s):  
Qiang Fu ◽  
Yufan Zhuang ◽  
Jiaxin Gu ◽  
Yushu Zhu ◽  
Xin Guo
Keyword(s):  
Topic Modeling ◽  
Modeling Methods ◽  
Agreeing To Disagree

A case study in text mining: Textual analysis of the Territorial Papers

2019 ◽  
Author(s):  
Johannes Ledolter ◽  
Lea VanderVelde
Keyword(s):  
Text Mining ◽  
Federal Government ◽  
Topic Modeling ◽  
The United States ◽  
State Governments ◽  
Textual Information ◽  
Modeling Methods ◽  
Frontier Settlers ◽  
Initial Statistical Analysis

Abstract The Territorial Papers of the United States are a valuable and underused resource containing almost 10,000 documents written between 1789 and 1848 about the formation of new sovereign states from US territory. These communications between the federal government and frontier settlers comprise the actual discourse of the nation’s expansion over six decades. Digitizing the Territorial Papers permits the possibility of analyzing the entire corpus globally. Text mining and topic modeling methods give us a lens on the language patterns through which new state governments and the expanding nation were formed. An initial statistical analysis of the textual information provides a visualization of content, helps discern how ideals about governance emerged, and lays the foundation for developing more sophisticated hypotheses and theoretical constructs.

Evaluation of clustering and topic modeling methods over health-related tweets and emails

2021 ◽  
pp. 102096
Author(s):  
Juan Antonio Lossio-Ventura ◽  
Sergio Gonzales ◽  
Juandiego Morzan ◽  
Hugo Alatrista-Salas ◽  
Tina Hernandez-Boussard ◽  
...  
Keyword(s):  
Topic Modeling ◽  
Modeling Methods ◽  
Health Related

Multiple Relational Topic Modeling for Noisy Short Texts

2018 ◽  
Vol 28 (11n12) ◽  
pp. 1559-1574 ◽  
Author(s):  
Zheng Liu ◽  
Chiyu Liu ◽  
Bin Xia ◽  
Tao Li
Keyword(s):  
Social Networks ◽  
Random Sampling ◽  
Topic Modeling ◽  
Topic Model ◽  
State Of The Art ◽  
Sampling Strategy ◽  
Social Analysis ◽  
High Quality ◽  
Modeling Methods ◽  
Latent Topics

Understanding contents in social networks by inferring high-quality latent topics from short texts is a significant task in social analysis, which is challenging because social network contents are usually extremely short, noisy and full of informal vocabularies. Due to the lack of sufficient word co-occurrence instances, well-known topic modeling methods such as LDA and LSA cannot uncover high-quality topic structures. Existing research works seek to pool short texts from social networks into pseudo documents or utilize the explicit relations among these short texts such as hashtags in tweets to make classic topic modeling methods work. In this paper, we explore this problem by proposing a topic model for noisy short texts with multiple relations called MRTM (Multiple Relational Topic Modeling). MRTM exploits both explicit and implicit relations by introducing a document-attribute distribution and a two-step random sampling strategy. Extensive experiments, compared with the state-of-the-art topic modeling approaches, demonstrate that MRTM can alleviate the word co-occurrence sparsity and uncover high-quality latent topics from noisy short texts.

scholarly journals Applying Clustering and Topic Modeling to Automatic Analysis of Citizens’ Comments in EGovernment

2020 ◽  
Vol 12 (6) ◽  
pp. 1-10
Author(s):  
Gunay Y. Iskandarli ◽  
Keyword(s):  
Semantic Similarity ◽  
Topic Modeling ◽  
Clustering Algorithms ◽  
Automatic Analysis ◽  
Large Set ◽  
Modeling Methods

The paper proposes an approach to analyze citizens' comments in e-government using topic modeling and clustering algorithms. The main purpose of the proposed approach is to determine what topics are the citizens' commentaries about written in the e-government environment and to improve the quality of e-services. One of the methods used to determine this is topic modeling methods. In the proposed approach, first citizens' comments are clustered and then the topics are extracted from each cluster. Thus, we can determine which topics are discussed by citizens. However, in the usage of clustering and topic modeling methods appear some problems. These problems include the size of the vectors and the collection of semantically related of documents in different clusters. Considering this, the semantic similarity of words is used in the approach to reduce measure. Therefore, we only save one of the words that are semantically similar to each other and throw the others away. So, the size of the vector is reduced. Then the documents are clustered and topics are extracted from each cluster. The proposed method can significantly reduce the size of a large set of documents, save time spent on the analysis of this data, and improve the quality of clustering and LDA algorithm.

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