Semi-automated Methods for the Annotation and Design of a Semantic Network Designed for Sentiment Analysis of Social Web Content

2011 ◽  
Author(s):  
Donato Barbagallo ◽  
Leonardo Bruni ◽  
Chiara Francalanci ◽  
Paolo Giacomazzi ◽  
Francesco Merlo ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Semantic Network ◽  
Social Web ◽  
Web Content ◽  
Automated Methods

Mobile Social Web; Opportunities and Drawbacks

2011 ◽  
pp. 2500-2510
Author(s):  
Thorsten Caus ◽  
Stefan Christmann
Keyword(s):  
Online Communities ◽  
Success Factors ◽  
Mobile Internet ◽  
Social Web ◽  
Web Content ◽  
Mobile Web ◽  
Technical Requirements ◽  
Revenue Sources ◽  
Usage Patterns ◽  
Economic Perspectives

As mobile Internet usage continues to grow, the phenomenon of accessing online communities through mobile devices draws researchers’ attention. Statistics show that close to 60 percent of all mobile Internet traffic worldwide is related to the use of mobile social networks. In this chapter, the mobile social web is defined, categories of mobile communities explained, and success factors and drawbacks discussed from the technical, social, and economic perspectives. Challenges, including low transmission rates, changes in usage patterns, search for new revenue sources, as well as the need for development of original mobile web content and applications are addressed. The technical requirements for the mobile use of online communities are identified. The chapter closes with a summary of potential economic and social prospects of the emerging mobile social web.

scholarly journals Improving accessibility of CMS-based websites using automated methods

2020 ◽  
Author(s):  
Balázs Csontos ◽  
István Heckl
Keyword(s):  
Data Entry ◽  
Content Management ◽  
Management Systems ◽  
Web Content ◽  
New Methods ◽  
User Groups ◽  
Accessibility Issues ◽  
Accessibility Guidelines ◽  
Content Accessibility ◽  
Automated Methods

AbstractNew methods of identifying and fixing accessibility issues on websites are presented in this article. The websites taken into consideration by the research are created with content management systems (like WordPress or Joomla!). Our main goal was to develop different methods to improve accessibility that may be used by various user groups (website creators, operators, content editors). Some of our methods are easy to use, some need more proficiency. The three methods we have developed (CSS/SCSS class override, MVC-based extension override, HTML output override) are described in detail. The use of an already existing method (Data entry checking) is also introduced, as well as some development options of this method. Each method is introduced in general, furthermore an example of their usage is also presented. Using the proposed methods, websites can fulfil the recommendations of the Web Content Accessibility Guidelines (WCAG) in order to make the content of the websites more accessible.

Public Security Sentiment Analysis on Social Web

2021 ◽  
Vol 13 (1) ◽  
pp. 1-20
Author(s):  
Victor Diogho Heuer de Carvalho ◽  
Ana Paula Cabral Seixas Costa
Keyword(s):  
Literature Review ◽  
Sentiment Analysis ◽  
Research Agenda ◽  
Web Mining ◽  
Initial Time ◽  
Public Security ◽  
Social Web ◽  
Future Studies ◽  
Analytical Process ◽  
Literature Material

This article presents (1) the results of a literature review on social web mining and sentiment analysis on public security; (2) the idea of a framework for the analytical process involved in the literature review themes; and (3) a research agenda with a perspective for future studies, considering some elements of the analytical process. The literature review was based on searches of five databases: Scopus, IEEE Xplore, Web of Science, ScienceDirect, and Springer Link. Search strings were applied to retrieve literature material of four kinds, without defining an initial time milestone, to get the historical register of publications associated with the main thematic. After some filtering, primary and secondary findings were separated, enabling the identification of elements for the framework. Finally, the research agenda is presented, containing a set of three research artifacts related to the proposed framework.

Mobile Social Web

2010 ◽  
pp. 11-21
Author(s):  
Thorsten Caus ◽  
Stefan Christmann
Keyword(s):  
Online Communities ◽  
Success Factors ◽  
Mobile Internet ◽  
Social Web ◽  
Web Content ◽  
Mobile Web ◽  
Technical Requirements ◽  
Revenue Sources ◽  
Usage Patterns ◽  
Economic Perspectives

As mobile Internet usage continues to grow, the phenomenon of accessing online communities through mobile devices draws researchers’ attention. Statistics show that close to 60 percent of all mobile Internet traffic worldwide is related to the use of mobile social networks. In this chapter, the mobile social web is defined, categories of mobile communities explained, and success factors and drawbacks discussed from the technical, social, and economic perspectives. Challenges, including low transmission rates, changes in usage patterns, search for new revenue sources, as well as the need for development of original mobile web content and applications are addressed. The technical requirements for the mobile use of online communities are identified. The chapter closes with a summary of potential economic and social prospects of the emerging mobile social web.

Sentiment Analysis of Book Reviews Using Semantic Network

2020 ◽  
Vol 5 (3) ◽  
pp. 49-54
Author(s):  
Yogeshwaran T ◽  
Neena Jasmine S ◽  
Kishore Kumar B ◽  
Saraswathi S
Keyword(s):  
Sentiment Analysis ◽  
Semantic Network

scholarly journals Changes of the Public Attitudes of China to Domestic COVID-19 Vaccination After the Vaccines Were Approved: A Semantic Network and Sentiment Analysis Based on Sina Weibo Texts

2021 ◽  
Vol 9 ◽  
Author(s):  
Hao Gao ◽  
Difan Guo ◽  
Jing Wu ◽  
Qingting Zhao ◽  
Lina Li
Keyword(s):  
Sentiment Analysis ◽  
Semantic Network ◽  
Negative Emotion ◽  
Public Attitudes ◽  
Production Capacity ◽  
Vaccine Hesitancy ◽  
Chinese Government ◽  
The Public ◽  
Before And After ◽  
Public Concerns

Introduction: On December 31, 2020, the Chinese government announced that the domestic coronavirus disease-2019 (COVID-19) vaccines have obtained approval for conditional marketing and are free for vaccination. This release drove the attention of the public and intense debates on social media, which reflected public attitudes to the domestic vaccine. This study examines whether the public concerns and public attitudes to domestic COVID-19 vaccines changed after the official announcement.Methods: This article used big data analytics in the research, including semantic network and sentiment analysis. The purpose of the semantic network is to obtain the public concerns about domestic vaccines. Sentiment analysis reflects the sentiments of the public to the domestic vaccines and the emotional changes by comparing the specific sentiments shown on the posts before and after the official announcement.Results: There exists a correlation between the public concerns about domestic vaccines before and after the official announcement. According to the semantic network analysis, the public concerns about vaccines have changed after the official announcement. The public focused on the performance issues of the vaccines before the official approval, but they cared more about the practical issues of vaccination after that. The sentiment analysis showed that both positive and negative emotions increased among the public after the official announcement. “Good” was the most increased positive emotion and indicated great public appreciation for the production capacity and free vaccination. “Fear” was the significantly increased negative emotion and reflected the public concern about the safety of the vaccines.Conclusion: The official announcement of the approval for marketing improved the Chinese public acceptance of the domestic COVID-19 vaccines. In addition, safety and effectiveness are vital factors influencing public vaccine hesitancy.

scholarly journals Violence Detection over Online Social Networks: An Arabic Sentiment Analysis Approach

2021 ◽  
Vol 15 (14) ◽  
pp. 90
Author(s):  
Monther Khalafat ◽  
Ja'far S. Alqatawna ◽  
Rizik M. H. Al-Sayyed ◽  
Mohammad Eshtay ◽  
Thaeer Kobbaey
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Web Content ◽  
Web Content Mining ◽  
Social Media Networks ◽  
Violence Detection ◽  
The Social ◽  
Machine Learning Approach ◽  
Content Mining

<p class="0abstract">Today, the influence of the social media on different aspects of our lives is increasing, many scholars from various disciplines and majors looking at the social media networks as the ongoing revolution. In Social media networks, many bonds and connections can be established whether being direct or indirect ties. In fact, Social networks are used not only by people but also by companies. People usually create their own profiles and join communities to discuss different common issues that they have interest in. On the other hand, companies also can create their virtual presence on the social media networks to benefit from this media to understand the customers and gather richer information about them. With all of the benefits and advantages of social media networks, they should not always be seen as a safe place for communicating, sharing information and ideas, and establishing virtual communities. These information and ideas could carry with them hatred speeches that must be detected to avoid raising violence. Therefore, web content mining can be used to handle this issue. Web content mining is gaining more concern because of its importance for many businesses and institutions.  Sentiment Analysis (SA) is an important sub-area of web content mining.  The purpose of SA is to determine the overall sentiment attitude of writer towards a specific entity and classify these opinions automatically. There are two main approaches to build systems of sentiment analysis: the machine learning approach and the lexicon-based approach. This research presents the design and implementation for violence detection over social media using machine learning approach. Our system works on Jordanian Arabic dialect instead of Modern Standard Arabic (MSA). The data was collected from two popular social media websites (Facebook, Twitter) and has used native speakers to annotate the data. Moreover, different preprocessing techniques have been used to show their effect on our model accuracy. The Arabic lexicon was used for generating feature vectors and separate them to features set. Here, we have three well known machine learning algorithms: Support Vector Machine (SVM), Naive Bayes (NB) and k-Nearest Neighbors (KNN). Building on this view, Information Science Research Institute’s (ISRI) stemming and stop word file as a result of preprocessing were used to extract the features. Indeed, several features have been extracted; however, using the SVM classifier reveals that unigram and features extracted from lexicon are characterized by the highest accuracy to detect violence.</p>

scholarly journals MODELLING SEMANTIC KERNEL OF WEB RESOURCE

2021 ◽  
pp. 52-55
Author(s):  
Sergey Orekhov ◽  
Hennadiy Malyhon
Keyword(s):  
Distance Function ◽  
Virtual Instrument ◽  
Semantic Network ◽  
Maximum Flow ◽  
Web Content ◽  
Web Resource ◽  
Alternative Approach ◽  
The Moment ◽  
Content Processing ◽  
The Right

The article presents an attempt to describe mathematically the effect of the semantic kernel of a web resource on the Internet. In accordance with the theory of marketing, the product that we want to sell on the network is characterized by the following basic properties: price, time and place. In other words, a potential buyer wants to receive a given product in the right place at a given time. To satisfy this need, it is necessary to use the classic component of marketing, product promotion. However, this component is now becoming a fully virtual instrument. This tool functions in a hypertext, video and image environment. Therefore, the user analyzes the meaning of these elements in order to get the desired product. The results of web projects carried out in this area indicate the emergence of a new phenomenon, which reflects the main meaning of virtual promotion – this is the semantic core. The core is a short annotation of the main properties of the product, its location and time of appearance. Therefore, the purpose of this article is both a presentation of a new object of research and a mathematical description. It is assumed that the semantic core is formed on the basis of natural language terms. In other words, the semantic core is a set of keywords that are grouped by meaning. We propose to use data mining approaches for clustering to group terms. The classic clustering method at the moment is k-means. The article presents a model of the semantic core based on this method. This method and its distance function are considered as the second stage of web content processing. At the first stage, web content is converted into a semantic web. However, the k-means technique has significant drawbacks when modeling the semantic core. Therefore, in the development of this idea, the work shows an alternative way to modeling the kernel. As an alternative approach, the construction of clusters based on the concept of maximum flow is considered. This approach has the significant advantage that the type of links in the semantic network overlaps with the type of distance function in this method. As a result, on a real web project, the effect of the connection between the semantic core model and the level of new users of the web resource was demonstrated over the past five years. Keywords: semantic kernel, keyword, k-means, max flow.

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