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.

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 Twitter Networks’ Centrality Measures of the Association of Health Journalist (ANIS): the Case of Vaccines - Introduction and Theoretical Framework

2020 ◽  
Vol 3 (2) ◽  
pp. 12
Author(s):  
Miguel Martín Cárdaba ◽  
Rafael Carrasco Polaino ◽  
Ubaldo Cuesta Cambra
Keyword(s):  
Social Networks ◽  
Public Opinion ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Theoretical Framework ◽  
Communication Strategy ◽  
Centrality Measures ◽  
The Social ◽  
One Year

The popularization of Internet and the rise of social networks have offered an unbeatable opportunity for anti-vaccines, especially active communicators, to spread their message more effectively causing a significant impact on public opinion. A great amount of research has been carried out to understand the behavior that anti-vaccine communities show on social networks. However, it seems equally relevant to study the behavior that communities and communicators “pro vaccines” perform in these networks. Therefore, the objective of this research has been to study how members of the Spanish Association of Health Journalist (ANIS) communicate and use the social network Twitter. More specifically, the different interactions made by ANIS partners were analyzed through the so-called “centrality measures of social network analysis” (SNA), to check the configuration of the user network and detect those most relevant by their indexes of centrality, intermediation or mentions received. The research monitored 142 twitter accounts for one year analyzing 254 twits and their 2.671 interactions. The research concluded that the network of ANIS partners on Twitter regarding vaccines has little cohesion and has several components not connected to each other, in addition to the fact that there are users outside the association that show greater relevance than the partners themselves. We also concluded that there are an important lack of planning and direction in the communication strategy of ANIS on Twitter, which limits the dissemination of important content.

scholarly journals The Role of Social Networks in Creating Public Opinion About Electronic Study

2021 ◽  
Vol 8 (1) ◽  
pp. 379-397
Author(s):  
Shanaz Sadeq Mohamad ◽  
Sara Mohsen Qadir
Keyword(s):  
Social Networks ◽  
Public Opinion ◽  
Social Network ◽  
Survey Methodology ◽  
The Public ◽  
The Social ◽  
The Subject ◽  
Study Process ◽  
Electronic Education

In line with the developments of various social networks, it has made the public see a change for all the various issues in the nation, one of which was the issue of electronic education, which has been influenced by the social networks, especially by students. Therefore, from this perspective, the researcher in the research scientifically shows the role of the social networks in creating public opinion about the process of electronic study. This research is a description, a researcher who has used the research to achieve detailed and necessary data and information about the subject of survey methodology research. Among the students of Kurdistan University, Salahaddin University and World University students are research samples of 422 students of both maleand female genders, the most important results that researchers have reached are the social networks that are a reason for creating public opinion and all The data spread through the social network to a process have created public opinion about the electronic study process, the strongest network, the Facebook social network to create public opinion in Kurdistan. In the short list of research, recommendations and suggestions have been made.

scholarly journals Relacionando Modelagem de Tópicos e Classificação de Sentimentos para Análise de Mensagens do Twitter Durante a Pandemia da COVID-19

2020 ◽  
Author(s):  
Matheus Adler Soares Pinto ◽  
Antonio Fernando Lavareda Jacob Junior ◽  
Antonio José G. Busson ◽  
Sérgio Colcher
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
The Social ◽  
Modeling Techniques ◽  
Medical Advances

In 2020, COVID-19 pandemic is one of the most talked-about subjects on social networks. This subject has generated discussions of great importance about politics, economics, medical advances, people’s awareness, preventive techniques, etc. Using sentiment analysis and topic modeling techniques, in this paper, we aim to present an analysis of the messages from the social network Twitter during the pandemic of COVID-19. For this, we use a tweets dataset to train a sentiment classifier and then use the NMF algorithm to perform the interest topic generation.

scholarly journals Overcoming Language Variation in Sentiment Analysis with Social Attention

2017 ◽  
Vol 5 ◽  
pp. 295-307 ◽  
Author(s):  
Yi Yang ◽  
Jacob Eisenstein
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Sentiment Analysis ◽  
Network Architecture ◽  
Language Variation ◽  
Neural Network Architecture ◽  
The Social ◽  
Classification Function

Variation in language is ubiquitous, particularly in newer forms of writing such as social media. Fortunately, variation is not random; it is often linked to social properties of the author. In this paper, we show how to exploit social networks to make sentiment analysis more robust to social language variation. The key idea is linguistic homophily: the tendency of socially linked individuals to use language in similar ways. We formalize this idea in a novel attention-based neural network architecture, in which attention is divided among several basis models, depending on the author’s position in the social network. This has the effect of smoothing the classification function across the social network, and makes it possible to induce personalized classifiers even for authors for whom there is no labeled data or demographic metadata. This model significantly improves the accuracies of sentiment analysis on Twitter and on review data.

scholarly journals WORD2VEC AND BERT LANGUAGE MODELS USED FOR A SENTIMENT ANALYSIS OF TEXT POSTS IN SOCIAL NETWORKS

2020 ◽  
Vol 61 (3) ◽  
pp. 60-69
Author(s):  
Nadezhda G. Yarushkina ◽  
◽  
Vadim S. Moshkin ◽  
Andrei A. Konstantinov ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Training Sample ◽  
Text Messages ◽  
Russian Language ◽  
Language Models ◽  
Original Algorithm ◽  
Text Preprocessing

The paper proposes an original algorithm for the formation of a training sample for a neural network that provides a sentiment analysis of text posts in social networks. A feature of the algorithm is the use of the extended Russian-language semantic thesaurus WordNetAffect and the expert dictionary of author’s symbols for expressing emotions. In addition, the paper describes the application of a neural network based on the LSTM architecture to determine the emotional coloring of text messages on a social network using two text vectorization algorithms “word2vec” and “BERT”. As a result of the experiments, an indicator of the accuracy of determining the emotional coloring of messages of 87% was achieved using lemmatization as a text preprocessing algorithm and the BERT algorithm when converting it into a vector.

scholarly journals A Sentiment Analysis of Brazilian Elections Tweets

2020 ◽  
Author(s):  
André Cristiani ◽  
Douglas Lieira ◽  
Heloisa Camargo
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Strong Relationship ◽  
Support Vector ◽  
Learning Approaches ◽  
Sources Of Information ◽  
Good Source ◽  
The Social ◽  
Source Of Information

The internet connection is present in people’s lives all the time, through smartphones, tablets, computers, among others. The use of social networks is increasingly common around the world. Many companies and people use them to spread products and services and publish opinions, facts that have turned the social networks into powerful sources of information on various topics. Identifying these feelings is a great strategy for many types of decision making. Thus, the purpose of this paper is to collect messages from a specific social network, in this case Twitter, referring to the 2018 Brazilian presidential elections and classify them as: positive, negative and neutral, in order to discover a possible relationship between opinions of social network users and the final outcome of the elections. For this, a corpus was built, preprocessed and evaluated by two different machine learning approaches: Naive Bayes and SVM (Support Vector Machine). The results showed that this social network is a good source of information to perform sentiment analysis and that the number of tweets classified as positive have a strong relationship with the researchers and the final result of the 2018 elections.

Emotive Trending And Tracking of Tweets

2017 ◽  
Vol 7 (11) ◽  
pp. 25
Author(s):  
Sanjay Chhataru Gupta
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
The Social ◽  
The World ◽  
Complex Events

Popularity of the social media and the amount of importance given by an individual to social media has significantly increased in last few years. As more and more people become part of the social networks like Twitter, Facebook, information which flows through the social network, can potentially give us good understanding about what is happening around in our locality, state, nation or even in the world. The conceptual motive behind the project is to develop a system which analyses about a topic searched on Twitter. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. The system tracks changes in emotions over events, signalling possible flashpoints or abatement. For each trending topic, the system also shows a sentiment graph showing how positive and negative sentiments are trending as the topic is getting trended.

The Oxford Handbook of Social Networks

2020 ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Life ◽  
Information Science ◽  
Computational Social Science ◽  
The Social ◽  
Diverse Field ◽  
The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

Comparing the social networks of service users with long term mental health needs living in community with those in a general adult in-patient unit

2021 ◽  
pp. 002076402110175
Author(s):  
Roberto Rusca ◽  
Ike-Foster Onwuchekwa ◽  
Catherine Kinane ◽  
Douglas MacInnes
Keyword(s):  
Mental Health ◽  
Social Networks ◽  
Social Network ◽  
Patient Group ◽  
Mental Health Problems ◽  
Well Being ◽  
Health Problems ◽  
Practical Support ◽  
The Social

Background: Relationships are vital to recovery however, there is uncertainty whether users have different types of social networks in different mental health settings and how these networks may impact on users’ wellbeing. Aims: To compare the social networks of people with long-term mental illness in the community with those of people in a general adult in-patient unit. Method: A sample of general adult in-patients with enduring mental health problems, aged between 18 and 65, was compared with a similar sample attending a general adult psychiatric clinic. A cross-sectional survey collected demographic data and information about participants’ social networks. Participants also completed the Short Warwick Edinburgh Mental Well-Being Scale to examine well-being and the Significant Others Scale to explore their social network support. Results: The study recruited 53 participants (25 living in the community and 28 current in-patients) with 339 named as important members of their social networks. Both groups recorded low numbers in their social networks though the community sample had a significantly greater number of social contacts (7.4 vs. 5.4), more monthly contacts with members of their network and significantly higher levels of social media use. The in-patient group reported greater levels of emotional and practical support from their network. Conclusions: People with serious and enduring mental health problems living in the community had a significantly greater number of people in their social network than those who were in-patients while the in-patient group reported greater levels of emotional and practical support from their network. Recommendations for future work have been made.

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