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 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 Digital sources: a case study of the analysis of the Recovery of Historical Memory in Spain on the social network Twitter

2019 ◽  
Vol 7 (2) ◽  
pp. 015 ◽  
Author(s):  
Mariluz Congosto
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Network ◽  
Historical Memory ◽  
Historical Research ◽  
Huge Amount ◽  
Online Social Media ◽  
The Social ◽  
Special Relevance

The incorporation of digital sources from online social media into historical research brings great opportunities, although it is not without technological challenges. The huge amount of information that can be obtained from these platforms obliges us to resort to the use of quantitative methodologies in which algorithms have special relevance, especially regarding network analysis and data mining. The Recovery of Historical Memory in Spain on the social network Twitter will be analysed in this article. An open-code tool called T-Hoarder was used; it is based on objectivity, transparency and knowledge-sharing. It has been in use since 2012.

Depressive Person Detection using Social Asian Elephants' (SAE) Algorithm over Twitter Posts

2019 ◽  
Vol 9 (4) ◽  
pp. 37-51
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Decision Tree ◽  
Social Life ◽  
Naive Bayes ◽  
Data Sources ◽  
Asian Elephants ◽  
Person Detection ◽  
The Social ◽  
The Web

With the advent of the web and the explosion of data sources such as opinion sites, blogs and microblogs appeared the need to analyze millions of posts, tweets or opinions in order to find out what thinks the net surfers. The idea was to produce a new algorithm inspired by the social life of Asian elephants to detect a person in depressive situation through the analysis of twitter social network. The proposal algorithm gives better performance compared to data mining and bioinspired techniques such as naive Bayes, decision tree, heart lungs algorithm, social cockroach's algorithm.

Sentiment analysis of foreign tourists to Bangkok using data mining through online social network

2017 ◽  
Author(s):  
Taweesak Kuhamanee ◽  
Nattaphon Talmongkol ◽  
Krit Chaisuriyakul ◽  
Wimol San-Um ◽  
Noppadon Pongpisuttinun ◽  
...  
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Sentiment Analysis ◽  
Online Social Network ◽  
Using Data

The Alt-social Network of Natalie Bookchin’s Testament

2016 ◽  
Vol 18 (5) ◽  
pp. 459-477
Author(s):  
Sarah Whitcomb Laiola
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Online Social Networks ◽  
Social Web ◽  
User Interactions ◽  
Media Criticism ◽  
Direct Translation ◽  
The Social

This article addresses issues of user precarity and vulnerability in online social networks. As social media criticism by Jose van Dijck, Felix Stalder, and Geert Lovink describes, the social web is a predatory system that exploits users’ desires for connection. Although accurate, this critical description casts the social web as a zone where users are always already disempowered, so fails to imagine possibilities for users beyond this paradigm. This article examines Natalie Bookchin’s composite video series, Testament, as it mobilizes an alt-(ernative) social network of vernacular video on YouTube. In the first place, the alt-social network works as an iteration of “tactical media” to critically reimagine empowered user-to-user interactions on the social web. In the second place, it obfuscates YouTube’s data-mining functionality, so allows users to socialize online in a way that evades their direct translation into data and the exploitation of their social labor.

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.

Understanding the expression of grievances in the Arabic Twitter-sphere using machine learning

2019 ◽  
Vol 5 (2) ◽  
pp. 108-119
Author(s):  
Yeslam Al-Saggaf ◽  
Amanda Davies
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Network ◽  
Network Analysis ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Content Type ◽  
The Social ◽  
Twitter Users

Purpose The purpose of this paper is to discuss the design, application and findings of a case study in which the application of a machine learning algorithm is utilised to identify the grievances in Twitter in an Arabian context. Design/methodology/approach To understand the characteristics of the Twitter users who expressed the identified grievances, data mining techniques and social network analysis were utilised. The study extracted a total of 23,363 tweets and these were stored as a data set. The machine learning algorithm applied to this data set was followed by utilising a data mining process to explore the characteristics of the Twitter feed users. The network of the users was mapped and the individual level of interactivity and network density were calculated. Findings The machine learning algorithm revealed 12 themes all of which were underpinned by the coalition of Arab countries blockade of Qatar. The data mining analysis revealed that the tweets could be clustered in three clusters, the main cluster included users with a large number of followers and friends but who did not mention other users in their tweets. The social network analysis revealed that whilst a large proportion of users engaged in direct messages with others, the network ties between them were not registered as strong. Practical implications Borum (2011) notes that invoking grievances is the first step in the radicalisation process. It is hoped that by understanding these grievances, the study will shed light on what radical groups could invoke to win the sympathy of aggrieved people. Originality/value In combination, the machine learning algorithm offered insights into the grievances expressed within the tweets in an Arabian context. The data mining and the social network analyses revealed the characteristics of the Twitter users highlighting identifying and managing early intervention of radicalisation.

Data Mining for Social Network Analysis Using a CLIQUE Algorithm

2019 ◽  
pp. 160-187
Author(s):  
Phu Ngoc Vo ◽  
Tran Vo Thi Ngoc
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
The Social ◽  
The World ◽  
Commercial Applications ◽  
Clique Algorithm

Many different areas of computer science have been developed for many years in the world. Data mining is one of the fields which many algorithms, methods, and models have been built and applied to many commercial applications and research successfully. Many social networks have been invested and developed in the strongest way for the recent years in the world because they have had many big benefits as follows: they have been used by lots of users in the world and they have been applied to many business fields successfully. Thus, a lot of different techniques for the social networks have been generated. Unsurprisingly, the social network analysis is crucial at the present time in the world. To support this process, in this book chapter we have presented many simple concepts about data mining and social networking. In addition, we have also displayed a novel model of the data mining for the social network analysis using a CLIQUE algorithm successfully.

Cognitive Social Mining Analysis Using Data Mining Techniques

2019 ◽  
pp. 99-117 ◽  
Author(s):  
Dharmpal Singh
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Statistical Modelling ◽  
Data Interpretation ◽  
Computer Mediated ◽  
The Social ◽  
Using Data ◽  
Collaborative Activities

Social media are based on computer-mediated technologies that smooth the progress of the creation and distribution of information, thoughts, idea, career benefits and other forms of expression via implicit communities and networks. The social network analysis (SNA) has emerged with the increasing popularity of social networking services like Facebook, Twitter, etc. Therefore, information about group cohesion, contribution in activities, and associations among subjects can be obtained from the analysis of the blogs. The analysis of the blogs required well-known knowledge discovery tools to help the administrator to discover participant collaborative activities or patterns with inferences to improve the learning and sharing process. Therefore, the goal of this chapter is to provide the data mining tools for information retrieval, statistical modelling and machine learning to employ data pre-processing, data analysis, and data interpretation processes to support the use of social network analysis (SNA) to improve the collaborative activities for better performance.

Sign in / Sign up

Export Citation Format

Share Document