Spatial Temporal Topic Embedding: A Semantic Modeling Method for Short Text in Social Network

2018 ◽  
pp. 198-210 ◽  
Author(s):  
Congxian Yang ◽  
Junping Du ◽  
Feifei Kou ◽  
Jangmyung Lee
Keyword(s):  
Social Network ◽  
Modeling Method ◽  
Semantic Modeling ◽  
Short Text

A semantic modeling method for social network short text based on spatial and temporal characteristics

2018 ◽  
Vol 28 ◽  
pp. 281-293 ◽  
Author(s):  
Feifei Kou ◽  
Junping Du ◽  
Zijian Lin ◽  
Meiyu Liang ◽  
Haisheng Li ◽  
...  
Keyword(s):  
Social Network ◽  
Modeling Method ◽  
Semantic Modeling ◽  
Short Text ◽  
Temporal Characteristics ◽  
Spatial And Temporal Characteristics

КОМУНІКАТИВНА КОМПЕТЕНТНІСТЬ У ПОЛІТИЧНОМУ ДИСКУРСІ (НА ПРИКЛАДІ ОФІЦІЙНИХ СТОРІНОК НАРОДНИХ ДЕПУТАТІВ У СОЦІАЛЬНІЙ МЕРЕЖІ «ФЕЙСБУК»)

2020 ◽  
pp. 4
Author(s):  
T. Sashchuk
Keyword(s):  
Social Network ◽  
Subject Matter ◽  
Communicative Competence ◽  
Professional Activity ◽  
First Person ◽  
Short Text ◽  
Scientific Methods ◽  
Content Analysis Method ◽  
General Scientific ◽  
Facebook Pages

<div><em>The article presents the results of the study of the communicative competence of the politicians on the basis of the analysis of their messages on their official pages of the Facebook social network. The research used the following general scientific methods: descriptive and comparative, as well as analysis, synthesis and generalization. The quantitative content analysis method with qualitative elements was used to distinguish the peculiarities of information messages that provide communication of the deputies of Verkhovna Rada (Ukrainian Parliament) on their official Facebook pages. Information messages have been analyzed by the following three criteria: subject matter, structure and language.</em></div><p> </p><p><em>For the first time the article draws a parallel between communicative competence and the ability to communicate with voters on the official pages of Facebook which is the most popular social network in Ukraine. As it is established, communicative competence in the analyzed cases is caused not by education, but by previous professional activity of a politician. The most successful and high-quality communication was from the current parliamentarian who worked as a journalist in the past. More than half of the messages that provided successful communication consisted of sufficiently structured short text and a video. The topic covers the activity of the parliamentarian in the Verkhovna Rada and in his district. More than half of the messages are spoken in the first person.</em></p><p><em>The findings of the study can be used in teaching such subjects as Political PR and Electronic PR, and may be of interest to politicians and their assistants.</em><em></em></p><p><strong><em>Key words:</em></strong><em> competence and competency, communicative competence, political discourse, official page of the deputy of Verkhovna Rada of Ukraine on the Facebook social network, subject matter and structure of the information message, first-person narrative, correspondence of communication to the level of communicative competence.</em></p>

scholarly journals A word embedding topic model for topic detection and summary in social networks

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1289-1298 ◽  
Author(s):  
Lei Shi ◽  
Gang Cheng ◽  
Shang-ru Xie ◽  
Gang Xie
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Topic Model ◽  
Word Embedding ◽  
Probabilistic Latent Semantic Analysis ◽  
Topic Detection ◽  
Short Text ◽  
Internal Relationship

The aim of topic detection is to automatically identify the events and hot topics in social networks and continuously track known topics. Applying the traditional methods such as Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis is difficult given the high dimensionality of massive event texts and the short-text sparsity problems of social networks. The problem also exists of unclear topics caused by the sparse distribution of topics. To solve the above challenge, we propose a novel word embedding topic model by combining the topic model and the continuous bag-of-words mode (Cbow) method in word embedding method, named Cbow Topic Model (CTM), for topic detection and summary in social networks. We conduct similar word clustering of the target social network text dataset by introducing the classic Cbow word vectorization method, which can effectively learn the internal relationship between words and reduce the dimensionality of the input texts. We employ the topic model-to-model short text for effectively weakening the sparsity problem of social network texts. To detect and summarize the topic, we propose a topic detection method by leveraging similarity computing for social networks. We collected a Sina microblog dataset to conduct various experiments. The experimental results demonstrate that the CTM method is superior to the existing topic model method.

First experiences with a semantic modeling method to guide simulation-based PACS development

1990 ◽  
Author(s):  
W. J. J. Stut, Jr. ◽  
M. R. van Steen ◽  
L. P. Groenewegen ◽  
Albert R. Bakker
Keyword(s):  
Modeling Method ◽  
Semantic Modeling ◽  
Simulation Based

scholarly journals Hashtag2Vec: Learning Hashtag Representation with Relational Hierarchical Embedding Model

2018 ◽  
Author(s):  
Jie Liu ◽  
Zhicheng He ◽  
Yalou Huang
Keyword(s):  
Social Networks ◽  
Information Retrieval ◽  
Social Network ◽  
Real World ◽  
Heterogeneous Network ◽  
Event Analysis ◽  
Short Text ◽  
Theme Discovery ◽  
Real World Datasets ◽  
Text Content

Hashtags have always been important elements in many social network platforms and micro-blog services. Semantic understanding of hashtags is a critical and fundamental task for many applications on social networks, such as event analysis, theme discovery, information retrieval, etc. However, this task is challenging due to the sparsity, polysemy, and synonymy of hashtags. In this paper, we investigate the problem of hashtag embedding by combining the short text content with the various heterogeneous relations in social networks. Specifically, we first establish a network with hashtags as its nodes. Hierarchically, each of the hashtag nodes is associated with a set of tweets and each tweet contains a set of words. Then we devise an embedding model, called Hashtag2Vec, which exploits multiple relations of hashtag-hashtag, hashtag-tweet, tweet-word, and word-word relations based on the hierarchical heterogeneous network. In addition to embedding the hashtags, our proposed framework is capable of embedding the short social texts as well. Extensive experiments are conducted on two real-world datasets, and the results demonstrate the effectiveness of the proposed method.

scholarly journals A Sentiment Delivering Estimate Scheme Based on Trust Chain in Mobile Social Network

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Meizi Li ◽  
Yang Xiang ◽  
Bo Zhang ◽  
Zhenhua Huang
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Sentiment Analysis ◽  
Significant Role ◽  
Evaluation Method ◽  
Modeling Method ◽  
Chain Model ◽  
Mobile Social Network ◽  
Trust Chain ◽  
Network Platform

User sentiment analysis has become a flourishing frontier in data mining mobile social network platform since the mobile social network plays a significant role in users’ daily communication and sentiment interaction. This study studies the scheme of sentiment estimate by using the users’ trustworthy relationships for evaluating sentiment delivering. First, we address an overview of sentiment delivering estimate scheme and propose its related definitions, that is, trust chain among users, sentiment semantics, and sentiment ontology. Second, this study proposes the trust chain model and its evaluation method, which is composed of evaluation of atomic, serial, parallel, and combined trust chains. Then, we propose sentiment modeling method by presenting its modeling rules. Further, we propose the sentiment delivering estimate scheme from two aspects: explicit and implicit sentiment delivering estimate schemes, based on trust chain and sentiment modeling method. Finally, examinations and results are given to further explain effectiveness and feasibility of our scheme.

A short text modeling method combining semantic and statistical information

2010 ◽  
Vol 180 (20) ◽  
pp. 4031-4041 ◽  
Author(s):  
Liu Wenyin ◽  
Xiaojun Quan ◽  
Min Feng ◽  
Bite Qiu
Keyword(s):  
Statistical Information ◽  
Modeling Method ◽  
Short Text
Sign in / Sign up

Export Citation Format

Share Document