scholarly journals Database “Childfree (antinatalist) communities in the social network VKontakte”

2021 ◽  
Vol 5 (2) ◽  
pp. 92-96
Author(s):  
Irina E. Kalabikhina ◽  
Evgeny P. Banin
Keyword(s):  
Social Network ◽  
Sentiment Analysis ◽  
Thematic Analysis ◽  
Latent Dirichlet Allocation ◽  
Absolute Amount ◽  
Graph Structure ◽  
Unique Identifier ◽  
Text Data ◽  
Structure Of Communities ◽  
The Social

The database contains an upload of text comments in Russian from the social network VKontakte in .csv format (UTF-8 encoding). The comments are collected from communities, which discuss pregnancy, childhood, motherhood, paternity, etc. The upload contains comments under the posts with which the interaction took place. The absolute amount of likes is used as a criterion (comments are collected where the number of likes is greater than or equal to 5). The text data is processed (stemmization and lemmatization). The data are suitable for thematic analysis (e.g. LDA — Latent Dirichlet Allocation), sentiment analysis of statements, modelling the graph structure of communities (the link_comment variable contains a unique identifier of the post, link_author contains a unique user identifier), and forming a dictionary of demographic connotation in Russian. Sentiment analysis of statements enables measuring the dynamics of «demographic temperature» in antinatalist communities. The database is a supplement to the publication Kalabikhina IE, Banin EP (2020) Database «Pro-family (pronatalist) communities in the social network VKontakte». Population and Economics 4(3): 98–130. https://doi.org/10.3897/popecon.4.e60915.

scholarly journals Database “Pro-family (pronatalist) communities in the social network VKontakte”

2020 ◽  
Vol 4 (3) ◽  
pp. 98-130
Author(s):  
Irina E. Kalabikhina ◽  
Evgeny P. Banin
Keyword(s):  
Social Network ◽  
Thematic Analysis ◽  
Latent Dirichlet Allocation ◽  
Absolute Number ◽  
Graph Structure ◽  
Text Data ◽  
Structure Of Communities ◽  
The Social ◽  
The Absolute ◽  
Dirichlet Allocation

The database contains uploading text comments from the social network VKontakte in .csv format (UTF-8 encoding). The comments are collected from communities discussing pregnancy, childhood, motherhood, etc. Uploading contains comments to posts with which the interaction took place. The absolute number of likes was used as a criterion (comments were collected where the number of likes is greater than or equal to 5). Text data was pre-processed (stemmization and lemmatization). The data is suitable for thematic analysis (e.g. LDA – Latent Dirichlet Allocation), for modelling the graph structure of communities (the link_comment variable contains a unique post identifier, link_author contains a unique user identifier), for analysis of tonalities of statements and formation of a dictionary of demographic connotation in Russian. Analysis of the tonalities of statements enables measuring the dynamics of “demographic temperature” in pro-family (pronatalist) communities.

scholarly journals Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Yanni Liu ◽  
Dongsheng Liu ◽  
Yuwei Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Smart City ◽  
Topic Model ◽  
Rapid Development ◽  
Mobile Internet ◽  
Time Curve ◽  
Public Opinions ◽  
The Social

With the rapid development of mobile Internet, the social network has become an important platform for users to receive, release, and disseminate information. In order to get more valuable information and implement effective supervision on public opinions, it is necessary to study the public opinions, sentiment tendency, and the evolution of the hot events in social networks of a smart city. In view of social networks’ characteristics such as short text, rich topics, diverse sentiments, and timeliness, this paper conducts text modeling with words co-occurrence based on the topic model. Besides, the sentiment computing and the time factor are incorporated to construct the dynamic topic-sentiment mixture model (TSTS). Then, four hot events were randomly selected from the microblog as datasets to evaluate the TSTS model in terms of topic feature extraction, sentiment analysis, and time change. The results show that the TSTS model is better than the traditional models in topic extraction and sentiment analysis. Meanwhile, by fitting the time curve of hot events, the change rules of comments in the social network is obtained.

scholarly journals Determining the proximity of groups in social networks based on text analysis using big data

2019 ◽  
pp. 521-526
Author(s):  
A S Mukhin ◽  
I A Rytsarev ◽  
R A Paringer ◽  
A V Kupriyanov ◽  
D V Kirsh
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Data Collection ◽  
Text Analysis ◽  
Software Tool ◽  
Text Data ◽  
The Social ◽  
Information Research ◽  
Definition Of

The article is devoted to the definition of such groups in social networks. The object of the study was selected data social network Vk. Text data was collected, processed and analyzed. To solve the problem of obtaining the necessary information, research was conducted in the field of optimization of data collection of the social network Vk. A software tool that provides the collection and subsequent processing of the necessary data from the specified resources has been developed. The existing algorithms of text analysis, mainly of large volume, were investigated and applied.

scholarly journals To the evolution model of network graph structure construction

2021 ◽  
pp. 1-16
Author(s):  
Yurii Nikolaevich Orlov ◽  
Alexander Seraphimovich Pankratov
Keyword(s):  
Social Network ◽  
High Dimension ◽  
Liouville Equation ◽  
Evolution Model ◽  
Structure Evolution ◽  
Graph Structure ◽  
Network Graph ◽  
The Social

In this paper the investigation of the structure of network graph is presented. The social network between the Russian towns is considered. It is shown, that the distribution of vertex powers is uniform. As a consequence there is a high dimension region with whole connection. The probability of special sub-graphs is estimated. The Liouville equation is used for modeling of the graph structure evolution.

scholarly journals Analysis of sentiments conveyed through Twitter concerning COVID-19

2021 ◽  
Vol 119 ◽  
pp. 07003
Author(s):  
Mohamed Chiny ◽  
Marouane Chihab ◽  
Omar Bencharef ◽  
Younes Chihab
Keyword(s):  
Social Network ◽  
Sentiment Analysis ◽  
High Frequency ◽  
Analysis Approach ◽  
Economic Aspects ◽  
The Social

Due to the social and economic fallout from the COVID-19 pandemic, we sought to gauge the attitudes of social network users, in this case, Twitter, towards the topic using a sentiment analysis approach. We collected 178,683 tweets using the Twitter API based on queries for the high-frequency hashtag #covid19. After the preprocessing step, we classified them in a binary way (positive and negative) and according to their intensity (valence) using the VADER model and then the NRCLex dictionary, which allows us to classify feelings according to their affective class. The results suggest that overall, the feelings detected through the tweets are positive. In addition, users seem to be interestedin the pandemic as a trend rather than as a topic related to other social or economic aspects.

scholarly journals Assorted Sentiment Model for Publically Available Page of Facebook

2020 ◽  
Vol 9 (3) ◽  
pp. 1160-1167
Keyword(s):  
Social Network ◽  
Sentiment Analysis ◽  
Business Strategy ◽  
English Language ◽  
Learning Algorithm ◽  
Indian Languages ◽  
Analysis Model ◽  
Indian Language ◽  
The Social ◽  
Bayes Algorithm

ecommerce industries expose public page in the social network site (Facebook, twitter etc) for the intention of improving of business strategy. They extract public mood about the social network page in the forms of total likes, the total share of the page and sentiment of all comments to the social network page similar way celebrities expose public page in the social network sites for the intention of improving its fame. We have developed an assorted model for publicly available page of Facebook. This assorted model is the combination of data extractor model, language convertor and cleaned model, and sentiment analyzer model. Our data extractor model extract comments on all the posts of publicly expose Facebook page in the less span of time. Language convertor and cleaned model would work for conversion of text written in different Indian language to the English language and after that English written text would be cleaned through cleaned model. Language convertor is made after implementing CILTEL model. CILTEL model converts comments written in the Indian languages in the English language. Cleaning model will clean all the comments of all the posts on the Facebook page. Finally, sentiment extraction model will extract sentiments of all the comments of the Facebook page. We have implemented classification using three machine learning algorithm, namely naïve bayes algorithm, perceptron algorithm and rocchio algorithm for checking the performance of our sentiment analysis model. Our assorted sentiment analysis model is beneficial to users like marketing industry, election parties and celebrities

scholarly journals Hot Topic Community Discovery on Cross Social Networks

2019 ◽  
Vol 11 (3) ◽  
pp. 60 ◽  
Author(s):  
Xuan Wang ◽  
Bofeng Zhang ◽  
Furong Chang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Latent Dirichlet Allocation ◽  
Rapid Development ◽  
Unified Model ◽  
Text Data ◽  
Community Discovery ◽  
Latent Topic ◽  
Discovery Method

The rapid development of online social networks has allowed users to obtain information, communicate with each other and express different opinions. Generally, in the same social network, users tend to be influenced by each other and have similar views. However, on another social network, users may have opposite views on the same event. Therefore, research undertaken on a single social network is unable to meet the needs of research on hot topic community discovery. “Cross social network” refers to multiple social networks. The integration of information from multiple social network platforms forms a new unified dataset. In the dataset, information from different platforms for the same event may contain similar or unique topics. This paper proposes a hot topic discovery method on cross social networks. Firstly, text data from different social networks are fused to build a unified model. Then, we obtain latent topic distributions from the unified model using the Labeled Biterm Latent Dirichlet Allocation (LB-LDA) model. Based on the distributions, similar topics are clustered to form several topic communities. Finally, we choose hot topic communities based on their scores. Experiment result on data from three social networks prove that our model is effective and has certain application value.

Research and Prototype Implementation of Network Group Sentiment Analysis Based on Topic

2014 ◽  
Vol 1042 ◽  
pp. 218-223
Author(s):  
Ya Hao He ◽  
Ya Ru Yang ◽  
Yu Zhong Qian ◽  
Jing Li
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Sentiment Analysis ◽  
Group Structure ◽  
Complex Relationship ◽  
Mining Technology ◽  
Prototype Implementation ◽  
The Social

In the era of Web2.0, people in the social network make up a complex relationship called group by communicating with others, such as forward or comment. Such networks are typically abundant with valuable information which can be mined. We use data mining technology to analyze the network group structure based on different topics, divide the network group into multiple sub-communities, analyze sentimenttendency of different communities, views and frequentpatterns, and present the overall characteristics of whole group visually to the users to help them make decisions.

scholarly journals Exploring Occupation Differences in Reactions to COVID-19 Pandemic on Twitter

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Yi Zhao ◽  
Haixu Xi ◽  
Chengzhi Zhang
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Experimental Results ◽  
Social Implications ◽  
Income Levels ◽  
Related Information ◽  
The Social ◽  
Twitter Users

AbstractCoronavirus disease 2019 (COVID-19) pandemic-related information are flooded on social media, and analyzing this information from an occupational perspective can help us to understand the social implications of this unprecedented disruption. In this study, using a COVID-19-related dataset collected with the Twitter IDs, we conduct topic and sentiment analysis from the perspective of occupation, by leveraging Latent Dirichlet Allocation (LDA) topic modeling and Valence Aware Dictionary and sEntiment Reasoning (VADER) model, respectively. The experimental results indicate that there are significant topic preference differences between Twitter users with different occupations. However, occupation-linked affective differences are only partly demonstrated in our study; Twitter users with different income levels have nothing to do with sentiment expression on covid-19-related topics.

scholarly journals The Measurement of Demographic Temperature Using the Sentiment Analysis of Data from the Social Network VKontakte

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 987
Author(s):  
Irina Evgenievna Kalabikhina ◽  
Evgeniy Petrovich Banin ◽  
Imiliya Abduselimovna Abduselimova ◽  
German Andreevich Klimenko ◽  
Anton Vasilyevich Kolotusha
Keyword(s):  
Social Networks ◽  
Public Opinion ◽  
Social Network ◽  
Sentiment Analysis ◽  
Russian Language ◽  
Temperature Ratio ◽  
Additional Result ◽  
Demographic Groups ◽  
The Social ◽  
Negative Comments

Social networks have a huge potential for the reflection of public opinion, values, and attitudes. In this study, the presented approach can allow to continuously measure how cold “the demographic temperature” is based on data taken from the Russian social network VKontakte. This is the first attempt to analyze the sentiment of Russian-language comments on social networks to determine the demographic temperature (ratio of positive and negative comments) in certain socio-demographic groups of social network users. The authors use generated data from the comments to posts from 314 pro-natalist groups (with child-born reproductive attitudes) and eight anti-natalist groups (with child-free reproductive attitudes) on the demographic topic, which have 9 million of users from all over Russia. The algorithm of the sentiment analysis for demographic tasks is presented in the article. In particularly, it was found that comments under posts are more suitable for analyzing the sentiment of statements than the texts of posts. Using the available data in two types of groups since 2014, we find an asynchronous structural shift in comments of the corpuses of pro-natalist and anti-natalist thematic groups. Interpretations of the evidences are offered in the discussion part of the article. An additional result of our work is two open Russian-language datasets of comments on social networks.

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