Semantic Network Analysis
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Semantic Network Analysis
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Author : Wouter van Atteveldt
language : en
Publisher:
Release Date : 2008
Semantic Network Analysis written by Wouter van Atteveldt and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Social Science categories.
This books describes a number of techniques that have been developed to facilitate Semantic Network Analysis. It describes techniques to automatically extract networks using co-occurrence, grammatical analysis, and sentiment analysis using machine learning. Additionally, it describes techniques to represent the extracted semantic networks and background knowledge about the actors and issues in the network, using Semantic Web techniques to deal with multiple issue categorisations and political roles and functions that shift over time. It shows how this combined network of message content and background knowledge can be queried and visualized to make it easy to answer a variety of research questions. Finally, this book describes the AmCAT infrastructure and iNet coding program for that have been developed to facilitate managing large automatic and manual content analysis projects.
Semantic Network Analysis In Social Sciences
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Author : Elad Segev
language : en
Publisher: Routledge
Release Date : 2021-11-29
Semantic Network Analysis In Social Sciences written by Elad Segev and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-29 with Psychology categories.
Semantic Network Analysis in Social Sciences introduces the fundamentals of semantic network analysis and its applications in the social sciences. Readers learn how to easily transform any given text into a visual network of words co-occurring together, a process that allows mapping the main themes appearing in the text and revealing its main narratives and biases. Semantic network analysis is particularly useful today with the increasing volumes of text-based information available. It is one of the developing, cutting-edge methods to organize, identify patterns and structures, and understand the meanings of our information society. The first chapters in this book offer step-by-step guidelines for conducting semantic network analysis, including choosing and preparing the text, selecting desired words, constructing the networks, and interpreting their meanings. Free software tools and code are also presented. The rest of the book displays state-of-the-art studies from around the world that apply this method to explore news, political speeches, social media content, and even to organize interview transcripts and literature reviews. Aimed at scholars with no previous knowledge in the field, this book can be used as a main or a supplementary textbook for general courses on research methods or network analysis courses, as well as a starting point to conduct your own content analysis of large texts.
Identifying Policy Frames Using Semantic Network Analysis
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Author : Chisung Park
language : en
Publisher:
Release Date : 2019
Identifying Policy Frames Using Semantic Network Analysis written by Chisung Park and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Policy sciences categories.
This case study demonstrates the identification and changes of policy frames in major countries as the policy context changes. This research has important implications for extracting policy frames from texts through semantic networks. Specifically, we want to share the researchers' concerns about how to code texts, how to identify subgroups, and how to reconstruct subgroups into policy frames. Readers are expected to gain better understanding of the usefulness of semantic network analysis in constructing policy arguments. This case discusses issues which could arise when disassembling the texts (i.e., constructing co-occurrence matrix), network analysis techniques needed for frame analysis (i.e., centrality and modularity analysis), and the processes to identify policy frames.
Methods Of Mapping And Analyzing Policy Networks Using Semantic Network Analysis
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Author : Aaron Lee Embrey
language : en
Publisher:
Release Date : 2012
Methods Of Mapping And Analyzing Policy Networks Using Semantic Network Analysis written by Aaron Lee Embrey and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Political science categories.
ABSTRACT: The primary objective of this study is to compare two methods of mapping and analyzing policy networks using semantic network analysis of textual information from media sources. The two methods compared were human coding and what the paper calls the automated computational method. This was accomplished by (1) using archival data as a source for policy network information, (2) determining whether the use of automated computational network mapping and analysis of archival media data compromised the accuracy and reliability of policy network results, and finally (3) by establishing whether the automated methods are a reliable tool to map and analyze policy networks. To compare the automated computational method with the human coding method, semantic results in a network matrix dataset are generated using AutoMap, a semantic network analysis program, and compared to the human coders' dataset from the same media sources. The comparative study revealed that the automated computational method identifies key terms at greater rates, requires significant preparation of data from media sources for reliable analyses, and presents errors affecting the network results. However, the characteristics of each network generated from the matrix data were similar but not identical. Correcting for the weaknesses of the automated computational method, researchers in the policy sciences and policy analysts may find a reliable and efficient method of mapping and analyzing policy networks of textual information from media sources.
Semantic Networks For Understanding Scenes
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Author : Gerhard Sagerer
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29
Semantic Networks For Understanding Scenes written by Gerhard Sagerer and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Computers categories.
Figure 1.1. An outdoor scene "A bus is passing three cars which are parking between trees at the side of the road. Houses having two storeys are lined up at the street. 3 4 Introduction Figure 1.2. An assembly scene There seems to be a small open place between the group of houses in the foreground and the store in the background". In such or a similar way the content of the natural scene shown above can be described. It is quite easy to give such a short description. The problem is somewhat more complex for the second image. First of all, it can be stated that the image does not show an everyday scene. It appears as a kind of man made surrounding. But everyone can accept the following statements about this image: 1. The image shows a snapshot of an assembly line. 2. The robot in front is screwing. 3. There is no person in the working area of the robots. 4. All objects on the conveyor belt are worked on by robots. There are no free objects on the belt.
A Data Driven Text Mining And Semantic Network Analysis For Design Information Retrieval
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Author : Feng Shi
language : en
Publisher:
Release Date : 2018
A Data Driven Text Mining And Semantic Network Analysis For Design Information Retrieval written by Feng Shi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Social Network Analysis And Text Mining For Big Data
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Author : Andrea Fronzetti Colladon
language : en
Publisher: Taylor & Francis
Release Date : 2025-06-20
Social Network Analysis And Text Mining For Big Data written by Andrea Fronzetti Colladon and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-20 with Business & Economics categories.
Social Network Analysis and Text Mining for Big Data presents cutting-edge methods and tools that bridge the gap between text mining and social network analysis research while also providing new insights for analyzing (big) textual and network data. These tools are designed to cater to the needs of both business analysts and researchers to facilitate the creation of groundbreaking analytics. Beginning with clear definitions of social network analysis and text mining, this book benefits from a thoughtfully curated selection of methods and tools, drawn from the authors’ extensive research in the field. The focus then shifts to demonstrate how the interplay between words and networks can unlock the full potential of big data analytics. A centerpiece of the book is the Semantic Brand Score (SBS), a versatile and powerful metric for assessing brand importance through text analysis. All of the above is corroborated and illustrated with practical applications and case studies showing the value of these analytics in supporting change and improved managerial decisions. It also introduces a specialized software tool which enables users to perform the analyses detailed in the text. This book is a must-read for business leaders, marketing professionals, policymakers, researchers, and university students. It offers practical insights and actionable advice for achieving increased performance of companies and societal actions. The writing is tailored to make complex concepts accessible to both experienced researchers and readers who are new to the field.
Semantic Network Analysis For Operating System Privacy And Security Using Twitter Data
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Author :
language : en
Publisher:
Release Date : 2020
Semantic Network Analysis For Operating System Privacy And Security Using Twitter Data written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computer networks categories.
As today's technologies are rapidly advancing, privacy and security issues have become one of the most significant concerns in digital communication. Thus, it is important to understand and draw a conceptual view that relies on how people perceive privacy and security in this current era of dynamic digital proliferation. This research aims to analyze and observe what people perceive about the privacy and security of online merchants, online social media, and other digital media. This research analyzes user interactions on one of the most widely used social media websites, Twitter. Primary data, including over 6 million tweets over twenty-five months was collected and analyzed to draw a conclusion. The study extracts meaningful data to aid in determining users' perception of privacy and security in the digital world based on personal experiences and encounters with privacy and security issues. This research focused on privacy and security issues posed by Twitter as a popular social media platform. In the initial analysis, we recorded the frequency of use of keywords in tweets. As a second step, we performed cleaning the tweets (data), all numbers, symbols, and other non-word text are filtered and removed. The research conducted further discourse analysis to determine how these words were emerging and correlating using TextRank, clustering and topic modeling algorithms. Furthermore, we classified sentiment of Twitter data by exhibiting results of machine learning using different algorithms. We tested three models to understand performance against the Naïve Bayes model. AdaBoost, Decision Tree, and Random Forest all underperformed in accuracy. Consequently, we developed a multilayer perceptron (MLP) model that would use one hidden layer. MLP was trained on 10 epochs and returned an accuracy of 83% on test data. This surpassed the Naïve Bayes model and results in high precision for neutral and positive tweets. MLP was one of the best algorithms, due to its feedforward artificial neural network that produced a series of outputs from a set of inputs. Although this algorithm is time consuming compared to the listing method, it can lead to relatively effective estimation. The tweets we examined are extracted and pre-processed and then categorized in neutral, negative, and positive sentiments. By applying the chosen methodology, the study was able to identify the most effective operating systems under study (i.e. Mac, iOS, Windows, and Android) in terms of privacy and security according to the sentiments of social media users. The approach will help us in simultaneously assessing the competitive intelligence of the Twitter data and the challenges in the form of privacy and security of the user content and their contextual information. The findings of the empirical research of the sentiment analysis shows that users are more concerned about the privacy of the iOS and Android compared to Mac and Windows. With a fully trained model, we made predictions on tweets including operating systems and the keywords "security" and "privacy". The selected operating systems were Mac, iOS, Windows, and Android. The results show that most tweets on these topics are positive except for iOS's privacy and Android's privacy, which both indicate a higher percentage of neutral and negative tweets. Mac's security showed the highest rate of postivie sentiment at 81.8% of tweets being categorized as positive.
Openness To Experience And Semantic Network Reorganization
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Author : Daniel Zeitlen
language : en
Publisher:
Release Date : 2022
Openness To Experience And Semantic Network Reorganization written by Daniel Zeitlen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Semantic memory has been increasingly studied using semantic network analysis, which models semantic memory structure (i.e., the organization of how concepts are related within semantic memory) as a network. While semantic memory is a dynamic cognitive system, few semantic network studies have assessed how semantic network structure can change with experience--a process central to learning new information. In the present study, we aimed to replicate the findings of a recent foundational study on semantic network reorganization--that a relational, but not attributive, conceptual combination task intervention resulted in reorganization of semantic networks, such that the flexibility and efficiency of the network structure was increased. We also examined how openness to experience, a personality trait linked to flexible thinking and semantic network structure, may impact semantic network reorganization. We followed the procedure of the original study and measured semantic memory using a free association task at two time points, before and after participants completed either a relational intervention (N = 114), an attributive intervention (N = 117), or no intervention in the baseline condition (N = 129). Semantic networks were modeled for each group (pre- and post-intervention), and we applied a bootstrapping method with 1,000 iterations to test differences in network structure between semantic networks. The present study replicated the semantic network reorganization results following the attributive intervention, but not in the relational or baseline conditions (which exhibited unexpected patterns of reorganization). Furthermore, we reran the semantic network analyses after subdividing conditions into high and low openness groups, and found that openness influenced the degree (but not pattern) of semantic network reorganization across conditions, with lower openness linked to greater reorganization. Altogether, the findings provide novel evidence on semantic network reorganization, including the first direct evidence linking changes in semantic network structure to an individual trait, and support the view that semantic memory structure is a dynamic and updating system.
Mapping Research Themes In Communication
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Author : Joseph Anthony Petrick
language : en
Publisher:
Release Date : 2014
Mapping Research Themes In Communication written by Joseph Anthony Petrick and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
The aim of the present research is to map research themes in communication through an analysis of papers delivered at annual meetings of the International Communication Association (ICA) from 2005-2010. Although there has been research in the related field of semantic network analysis, communication science lacks the tradition of research mapping that has been developed in information science and scientometric literature. The literature of co-word mapping in the latter disciplines will be examined, including how such mapping techniques are applied to disciplines other than information science and scientomentrics. To demonstrate this approach, research themes in information science will be derived from bibliographic citation data processed using CATPAC software and visually represented in UCINET maps. Subsequent to a use of CATPAC to map research themes in information science, the software will be used to analyze research themes in successive annual meetings of the ICA to demonstrate the incidence and evolution of research themes in papers delivered at the meetings. The maps will be determined by subject descriptors applied to individual papers, as well as keyword concepts taken from titles and abstracts of papers. Finally, mere exposure theory will be examined in relation to word frequency analysis, and concepts latent in individual conference papers will be examined.