Semantic knowledge LDA with topic vector for recommending hashtags: Twitter use case

Tajbakhsh, Mir Saman; Bagherzadeh, Jamshid

doi:10.3233/ida-183998

Cited by 29 publications

(15 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the NLP models based on semantic techniques that allows to discover semantic knowledge and translate social interpretations among online users in social media [36][37][38]. This paper aimed at identifying the key characters and interests that are mobilized of Incel online community.…”

Section: Discussion and Findingsmentioning

confidence: 99%

Semantic Knowledge Discovery and Discussion Mining of Incel Online Community: Topic modeling

Jelodar¹,

Frank²

2021

Preprint

View full text Add to dashboard Cite

Online forums provide a unique opportunity for online users to share comments and exchange information on a particular topic. Understanding user behaviour is valuable to organizations and has applications for social and security strategies, for instance, identifying user opinions within a community or predicting future behaviour. Discovering the semantic aspects in Incel forums are the main goal of this research; we apply Natural language processing techniques based on topic modeling to latent topic discovery and opinion mining of users from a popular online Incel discussion forum. To prepare the input data for our study, we extracted the comments from Incels.co. The research experiments show that Artificial Intelligence (AI) based on NLP models can be effective for semantic and emotion knowledge discovery and retrieval of useful information from the Incel community. For example, we discovered semantic-related words that describe issues within a large volume of Incel comments, which is difficult with manual methods.

show abstract

Section: Discussion and Findingsmentioning

confidence: 99%

Semantic Knowledge Discovery and Discussion Mining of Incel Online Community: Topic modeling

Jelodar¹,

Frank²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Topic model is popular and promising in the research area of data mining and natural language processing (Wang et al, 2016;Tajbakhsh and Bagherzadeh, 2019;Basilio et al, 2019Basilio et al, , 2020. Related researches have lasted for years.…”

Section: Related Workmentioning

confidence: 99%

SenU-PTM: a novel phrase-based topic model for short-text topic discovery by exploiting word embeddings

Zhang

2021

DTA

View full text Add to dashboard Cite

PurposeTopic model has been widely applied to discover important information from a vast amount of unstructured data. Traditional long-text topic models such as Latent Dirichlet Allocation may suffer from the sparsity problem when dealing with short texts, which mostly come from the Web. These models also exist the readability problem when displaying the discovered topics. The purpose of this paper is to propose a novel model called the Sense Unit based Phrase Topic Model (SenU-PTM) for both the sparsity and readability problems.Design/methodology/approachSenU-PTM is a novel phrase-based short-text topic model under a two-phase framework. The first phase introduces a phrase-generation algorithm by exploiting word embeddings, which aims to generate phrases with the original corpus. The second phase introduces a new concept of sense unit, which consists of a set of semantically similar tokens for modeling topics with token vectors generated in the first phase. Finally, SenU-PTM infers topics based on the above two phases.FindingsExperimental results on two real-world and publicly available datasets show the effectiveness of SenU-PTM from the perspectives of topical quality and document characterization. It reveals that modeling topics on sense units can solve the sparsity of short texts and improve the readability of topics at the same time.Originality/valueThe originality of SenU-PTM lies in the new procedure of modeling topics on the proposed sense units with word embeddings for short-text topic discovery.

show abstract

“…Latent Dirichlet allocation (LDA) is a document topic model that includes three levels, namely, document, topic, and word. The model can identify latent topic information in large document sets or corpus using unsupervised machine learning techniques [44,45]. The main idea involves treating each document as a mixed distribution of topics and each topic as a probability distribution of words.…”

Section: Classification Of Hot Cultural Topicsmentioning

confidence: 99%

Analysis of Spatial Interaction between Different Food Cultures in South and North China: Practices from People’s Daily Life

Zhang

Zhou

Huang

2020

IJGI

View full text Add to dashboard Cite

An important component of research in cultural geography involves the exploration and analysis of the laws of regional cultural differences. This topic has considerable significance in the discovery of distinctive cultures, protection of regional cultures, and in-depth understanding of cultural differences. In recent years, with the “spatial turn” of sociology, scholars have focused increasing attention to implicit spatial information in social media data, as well as the social phenomena and laws they reflect. Grasping sociocultural phenomena and their spatial distribution characteristics through texts is an important aspect. Using machine learning methods, such as the popular natural language processing (NLP) approach, this study extracts hotspot cultural elements from text data and accurately detects the spatial interaction patterns of specific cultures, as well as the characteristics of emotions toward non-native cultures. Through NLP, this study examines cultural differences among people from South and North China by analyzing 6128 answers to the question, “What are the differences between South and North China that you ever know?” posted on the Zhihu Q&A platform. Moreover, this study probes individuals’ emotions and cognition of cultural differences between South and North China in three aspects, namely, spatial interaction patterns of hotspot cultural elements, components of hotspot cultures, and emotional characteristics under the influence of cultural differences between the two regions. Results reveal that: (1) people from North and South China exhibit considerable differences in recognizing each other’s culture; (2) among numerous cultural differences, food culture is the most popular; and (3) people tend to have a negative attitude toward food cultures that differ from their own. These factors can shed light on regional cultural differences and help address cultural conflicts. In addition, this study provides effective solutions from a macro perspective, which has been challenging for new cultural geography.

show abstract

Semantic knowledge LDA with topic vector for recommending hashtags: Twitter use case

Cited by 29 publications

References 22 publications

Semantic Knowledge Discovery and Discussion Mining of Incel Online Community: Topic modeling

Semantic Knowledge Discovery and Discussion Mining of Incel Online Community: Topic modeling

SenU-PTM: a novel phrase-based topic model for short-text topic discovery by exploiting word embeddings

Analysis of Spatial Interaction between Different Food Cultures in South and North China: Practices from People’s Daily Life

Contact Info

Product

Resources

About