Social network analytics for contemporary business organizations:
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hershey, PA
IGI Global
[2018]
|
| Schriftenreihe: | Advances in business information systems and analytics (ABISA) book series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xviii, 321 Seiten Illustrationen, Diagramme |
| ISBN: | 9781522550976 |
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Datensatz im Suchindex
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| adam_text | Table of Contents
Preface...............................................................................xvi
Chapter 1
Social Network Analysis: Tools, Techniques, and Technologies............................1
Somya Jain, Jaypee Institute of Information Technology, India
Adwitiya Sinha, Jaypee Institute of Information Technology; India
Chapter 2
Social Networking Data Analysis Tools and Services.......................................19
Gopal Krishna, Aryabhatt Knowledge University, India
Chapter 3
, Social Implications of E-Government.......................................................35
Rimjhim, Indian Institute of Technology Patna, India
Vijay Kumar, Steel Authority of India Limited, India
Chapter 4
Thwarting Spam on Facebook: Identifying Spam Posts Using Machine Learning Techniques.....51
Arti Jain, Jaypee Institute of Information Technology, India
Reetika Gairola, Jaypee Institute of Information Technology, India
Shikha Jain, Jaypee Institute of Information Technology, India
AnujaArora, Jaypee Institute of Information Technology, India
Chapter 5
Impact of Sarcasm in Sentiment Analysis Methodology......................................71
Priscilla Souza Silva, Federal University of South and Southeast of Para, Brazil
Haroldo Barroso, Federal University of Sul and Sudeste of Pam, Brazil
Leila Weitzel, Fluminense Federal University, Brazil
Dilcielly Almeida Ribeiro, Universidade Federal do Sul e Sudeste do Pam, Brazil
José Santos, Federal University of Sul and Sudeste of Para, Brazil
Chapter 6
Analysis of Online Social Networks for the Identification of Sarcasm.....................92
Pulkit Mehndiratta, Jaypee Institute of Information Technology, India
Chapter 7
A Novel Algorithm for Sentiment Analysis of Online Movie Reviews.........................106
Bisma Shah, Jamia Hamdard, India
Farheen Siddiqui, Jamia Hamdard, India
Chapter 8
Authorship Attribution for Online Social Media...........................................141
Ritu Banga, Jaypee Institute of Information Technology, India
Akanksha Bhardwaj, Jaypee Institute of Information Technology, India
Sheng-Lung Peng, National Dong Hwa University, Taiwan
Gulshan Shrivastava, National Institute of Technology Patna, India
Chapter 9
Business-Oriented Analytics With Social Network of Things................................166
Pawan Kumar, Government of India, India
Adwitiya Sinha, Jaypee Institute of Information Technology, India
Chapter 10
Social Aware Cognitive Radio Networks: Effectiveness of Social Networks as a Strategic Tool for
Organizational Business Management.......................................................188
Anandakumar Haldorai, Akshaya College of Engineering and Technology, India
Arulmurugan Ramu, Bannari Amman Institute of Technology, India
Suriya Murugan, Velammal College of Engineering and Technology, India
Chapter 11
An Experimental Evaluation of Link Prediction for Movie Suggestions Using Social Media
Content..................................................................................203
Anu Taneja, Jaypee Institute of Information Technology, India
Bhawna Gupta, Jaypee Institute of Information Technology, India
Anuja Arora, Jaypee Institute of Information Technology, India
Chapter 12
Knowledge Discovery Using Data Stream Mining: An Analytical Approach.....................231
Prasanna Lakshmi Kompalli, Gokaraju Rangaraju Institute of Engineering and Technology,
India
Chapter 13
Trust and Credibility Analysis of Websites: Role of Trust and Credibility in Evaluating Online
Content.................................................................................259
Himani Bansal, Jaypee Institute of Information Technology, India
Prakhar Shukla, Jaypee Institute of Information Technology, India
Manav Dhar, Jaypee Institute of Information Technology, India
Compilation of References...............................................................287
About the Contributors..................................................................314
Index...................................................................................320
Detailed Table of Contents
Preface..............................................................................xvi
Chapter 1
Social Network Analysis: Tools, Techniques, and Technologies..........................1
Somya Jain, Jaypee Institute of Information Technology, India
Adwitiya Sinha, Jaypee Institute of Information Technology, India
Over the last decade, technology has thrived to provide better, quicker, and more effective platforms to
help individuals connect and disseminate information to other individuals. The increasing popularity of
these networks and its huge content in the form of text, images, and videos provides new opportunities
for data analytics in the context of social networks. This motivates data mining experts and researchers
to deploy various mining apparatus and application-specific tools for analysing the massive, intricate,
and dynamic social media knowledge. The research detailed in this chapter would entail major social
network concepts with data analysis techniques. Moreover, it gives insight to representation and modelling
of social networks with research datasets and tools.
Chapter 2
Social Networking Data Analysis Tools and Services............................................19
Gopal Krishna, Aryabhatt Knowledge University, India
Social networks have drawn remarkable attention from IT professionals and researchers in data sciences.
They are the most popular medium for social interaction. Online social networking (OSN) can be
defined as involving networking for fun, business, and communication. Social networks have emerged as
universally accepted communication means and boomed in turning this world into a global town. OSN
media are generally known for broadcasting information, activities posting, contents sharing, product
reviews, online pictures sharing, professional profiling, advertisements and ideas/opinion/sentiment
expression, or some other stuff based on business interests. For the analysis of the huge amount of data,
data mining techniques are used for identifying the relevant knowledge from the huge amount of data that
includes detecting trends, patterns, and rules. Data mining techniques, machine learning, and statistical
modeling are used to retrieve the information. For the analysis of the data, three methods are used: data
pre-processing, data analysis, and data interpretation.
Chapter 3
Social Implications of E-Government.........................................................35
Rimjhim, Indian Institute of Technology Patna, India
Vijay Kumar, Steel Authority of India Limited, India
With increased ICT (Information and Communication Technology), the day-to-day life is shifting online.
E-government is one of the crucial parts of the developing world. This chapter contains a detailed
introduction of e-government, which includes a broad definition, pillars, goals, stages, delivery models of
e-government, advantages, and disadvantages. The difference between e-governance and e-government is
also clarified. This chapter clears up the concept of e-government and how e-government can have impact
on society. Next is the background section, which covers the research and surveys done by UN (United
Nations) and other researchers in order to monitor and analyze the developments of e-government across
different countries, social implications of e-government, requirements to develop a robust e-government,
future scope of the e-government, etc. Finally, the direct and indirect implications of e-government are
stated along with a short review of the status of e-government across a few countries.
Chapter 4
Thwarting Spam on Facebook: Identifying Spam Posts Using Machine Learning Techniques........51
Arti Jain, Jaypee Institute of Information Technology, India
Reetika Gairola, Jaypee Institute of Information Technology, India
Shikha Jain, Jaypee Institute of Information Technology, India
Anuja Arora, Jaypee Institute of Information Technology, India
Spam on the online social networks (OSNs) is evolving as a prominent problem for the users of these
networks. Spammers often use certain techniques to deceive the OSN users for their own benefit. Facebook,
one of the leading OSNs, is experiencing such crucial problems at an alarming rate. This chapter presents
a methodology to segregate spam from legitimate posts using machine learning techniques: naïve Bayes
(NB), support vector machine (SVM), and random forest (RF), The textual, image, and video features are
used together, which wasn’t considered by the earlier researchers. Then, 1.5 million posts and comments
are extracted from archival and real-time Facebook data, which is then pre-processed using RStudio. A
total of 30 features are identified, out of which 10 are the best informative for identification of spam vs.
ham posts. The entire dataset is shuffled and divided into three ratios, out of which 80:20 ratio of training
and testing dataset provides the best result. Also, RF classifier outperforms NB and SVM by achieving
overall F-measure 89.4% on the combined feature set.
Chapter 5
Impact of Sarcasm in Sentiment Analysis Methodology.........................................71
Priscilla Souza Silva, Federal University of South and Southeast of Para, Brazil
Haroldo Barroso, Federal University of Sul and Sudeste of Para, Brazil
Leila Weitzel, Fluminense Federal University, Brazil
Dilcielly Almeida Ribeiro, Universidade Federal do Sul e Sudeste do Para, Brazil
José Santos, Federal University of Sul and Sudeste of Parti, Brazil
Sentiment of analysis is a study area applied to numerous environments (financial, political, academic,
business, and communication) whose purpose is to search for messages posted on social media, and through
these to identify and classify people’s opinions about particular item as positive or negative. Rating the
sentiment expressed in opinionated messages is such an important task that currently companies invest
a lot of money in collecting this type of information and the development of methods and techniques to
classify the sentiment that they express, so that they can use the results as useful information in preparing
marketing and sales strategies efficiently. However, one of the major problems facing the feelings of
analysis is the difficulty of methods to properly analyze messages with sarcastic and/or ironic content, as
these linguistic phenomena have the characteristic of transforming the polarity or meaning of a positive
or negative statement into its opposite.
Chapter 6
Analysis of Online Social Networks for the Identification of Sarcasm..........................92
Pulkit Mehndiratta, Jaypee Institute of Information Technology, India
With the ever-increasing acceptance of online social networks (OSNs), a new dimension has evolved
for communication amongst humans. OSNs have given us the opportunity to monitor and mine the
opinions of a large number of online active populations in real time. Many diverse approaches have been
proposed, various datasets have been generated, but there is a need of collective understanding of this
area. Researchers are working around the globe to find a pattern to judge the mood of the user; the still
serious problem of detection of irony and sarcasm in textual data poses a threat to the accuracy of the
techniques evolved till date. This chapter aims to help the reader to think and learn more clearly about the
aspects of sentiment analysis, social network analysis, and detection of irony or sarcasm in textual data
generated via online social networks. It argues and discusses various techniques and solutions available
in literature currently. In the end, the chapter provides some answers to the open-ended question and
future research directions related to the analysis of textual data.
Chapter 7
A Novel Algorithm for Sentiment Analysis of Online Movie Reviews..............................106
Bisma Shah, Jamia Hamdard, India
Farheen Siddiqui, Jamia Hamdard, India
Others’ opinions can be decisive while choosing among various options, especially when those choices
involve worthy resources like spending time and money buying products or services. Customers relying
on their peers’ past reviews on e-commerce websites or social media have drawn a considerable interest
to sentiment analysis due to realization of its commercial and business benefits. Sentiment analysis can
be exercised on movie reviews, blogs, customer feedback, etc. This chapter presents a novel approach to
perform sentiment analysis of movie reviews given by users on different websites. Also, challenges like
presence of thwarted words, world knowledge, and subjectivity detection in sentiments are addressed in
this chapter. The results are validated by using two supervised machine learning approaches, k-nearest
neighbor and naive Bayes, both on method of sentiment analysis without addressing aforementioned
challenges and on proposed method of sentiment analysis with all challenges addressed. Empirical results
show that proposed method outperformed the one that left challenges unaddressed.
Chapter 8
Authorship Attribution for Online Social Media..............................................141
Ritu Banga, Jaypee Institute of Information Technology, India
Akanksha Bhardwaj, Jaypee Institute of Information Technology, India
Sheng-Lung Peng, National Dong Uwa University, Taiwan
Gulshcin Shrivastava, National Institute of Technology Patna, India
This chapter gives a comprehensive knowledge of various machine learning classifiers to achieve
authorship attribution (AA) on short texts, specifically tweets. The need for authorship identification is
due to the increasing crime on the internet, which breach cyber ethics by raising the level of anonymity.
AA of online messages has witnessed interest from many research communities. Many methods such
as statistical and computational have been proposed by linguistics and researchers to identify an author
from their writing style. Various ways of extracting and selecting features on the basis of dataset have
been reviewed. The authors focused on n-grams features as they proved to be very effective in identifying
the true author from a given list of known authors. The study has demonstrated that AA is achievable
on the basis of selection criteria of features and methods in small texts and also proved the accuracy
of analysis changes according to combination of features. The authors found character grams are good
features for identifying the author but are not yet able to identify the author independently.
Chapter 9
Business-Oriented Analytics With Social Network of Things...................................166
Pawan Kumar, Government of India, India
Adwitiya Sinha, Jaypee Institute of Information Technology, India
In the modern era of technological advancements, internet of things (loT) and social network of things
(SNoT) have gained vitality with the extensive application of sensors for accumulation of socially relevant
data. A colossal amount of social data collected becomes unfeasible to process and deliver with progress
in time and domain. Therefore, a major problem lies in analysis, interpretation, and understanding of
the huge amount of social data. This challenge has been greatly leveraged by context-aware computing,
which permits storing context information so that meaningful analysis of data can be achieved. Also,
the importance of context-aware social networking and network diffusion is elaborated with the aim to
develop effective solutions to issues in this domain. The main concept here is people around a person
share common experiences with that person, which in turn can be made interactive, thereby leading to
collective and quick resolving of problems. Social network of things is closely coupled with context
awareness to make interpretation of big data easier and compatible to recent trends.
Chapter 10
Social Aware Cognitive Radio Networks: Effectiveness of Social Networks as a Strategic Tool for
Organizational Business Management..........................................................188
Anandakumar Haldorai, Akshaya College of Engineering and Technology, India
Arulmurugan Ramu, Bannari Amman Institute of Technology, India
Suriya Murugan, Velammal College of Engineering and Technology, India
The mobile networks seem to have a steady future in the direction of the recent emergence of socially aware
cognitive mobile networks. Their style and design are specifically made in improving shared spectrum
space access, in cooperative spectrum sensing, and in enhancing device-to-device communications.
Socially aware mobile networks do have enough potential to amass sufficient returns in the efficacy of the
spectrum and also to march and gain a considerable amount of increase in the capacity of the network.
Even though there are lot of gains in its potency to be reaped yet, still there seems to be enough challenges
that are both business- and technical-related that have to be taken care of. This chapter delves into the
cognitive radio (CR) and its social relations and also makes sufficient exploits in establishing a scheme
that will be based on social-based cooperative sensing scheme (SBC).
Chapter 11
An Experimental Evaluation of Link Prediction for Movie Suggestions Using Social Media
Content....................................................................................203
Ann Taneja, Jaypee Institute of Information Technology, India
Bhawna Gupta, Jaypee Institute of Information Technology, India
Anuja Arora, Jaypee Institute of Information Technology, India
The enormous growth and dynamic nature of online social networks have emerged to new research
directions that examine the social network analysis mechanisms. In this chapter, the authors have explored
a novel technique of recommendation for social media and used well known social network analysis
(SNA) mechanisms-link prediction. The initial impetus of this chapter is to provide general description,
formal definition of the problem, its applications, state-of-art of various link prediction approaches in
social media networks. Further, an experimental evaluation has been made to inspect the role of link
prediction in real environment by employing basic common neighbor link prediction approach on IMDb
data. To improve performance, weighted common neighbor link prediction (WCNLP) approach has
been proposed. This exploits the prediction features to predict new links among users of IMDb. The
evaluation shows how the inclusion of weight among the nodes offers high link prediction performance
and opens further research directions.
Chapter 12
Knowledge Discovery Using Data Stream Mining: An Analytical Approach.......................231
Prasanna Lakshmi Kompalli, Gokaraju Rangaraju Institute of Engineering and Technology,
India
In recent years, advancement in technologies has made it possible for most of the present-day organizations
to store and record large streams of data. Such data sets, which continuously and rapidly grow over time,
are referred to as data streams. Mining of such data streams is a unique opportunity and also a challenging
task. Data stream mining is a process of gaining knowledge from continuous and rapid records of data.
Due to increased streaming information, data stream mining has attracted the research community in
the recent past. There is voluminous literature that has been published in this domain over the past few
years. Due to this, isolating the correct study would be grueling task for researchers and practitioners.
While addressing a real-world problem, it would be difficult to find relevant information as it would be
hidden in data streams. This chapter tries to provide solution as it is an amalgamation of all techniques
used for data stream mining.
Chapter 13
Trust and Credibility Analysis of Websites: Role of Trust and Credibility in Evaluating Online
Content...................................................................................259
Himani Bansal, Jaypee Institute of Information Technology, India
Prakhar Shukla, Jaypee Institute of Information Technology, India
Manav Dhar, Jaypee Institute of Information Technology, India
Trust on any online information is psychosomatic and hidden by nature. The choice is in the hands of the
information seeker to consider, evaluate, and confirm the contents of the websites before using it. This
makes a sharp concern for websites dealing with sensitive topics like health, research, or academics. There
is no benchmark or tool that tells or characterises about making these “trust” decisions. Although web
users make such decisions after considering numerous factors, still there are no such criteria to fulfil the
underlying principle to deal with such decision making. This chapter is an effort to resolve the problem
of how to measure the content provided by any website in terms of its credibility. Various models have
been projected in this chapter to identify several factors pertaining to the credibility of content and users’
trust on any website and accordingly analyse the identified factors to assess the websites.
Compilation of References..........................................................287
About the Contributors.............................................................314
Index
320
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| discipline | Informatik Soziologie Wirtschaftswissenschaften |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV044912510 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T08:04:34Z |
| institution | BVB |
| isbn | 9781522550976 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030305999 |
| oclc_num | 1039839251 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-739 |
| owner_facet | DE-355 DE-BY-UBR DE-739 |
| physical | xviii, 321 Seiten Illustrationen, Diagramme |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | IGI Global |
| record_format | marc |
| series2 | Advances in business information systems and analytics (ABISA) book series |
| spelling | Social network analytics for contemporary business organizations Himani Bansal, Guishan Shrivastava, Gia Nhu Nguyen and Loredana-Mihaela Stanciu Hershey, PA IGI Global [2018] xviii, 321 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Advances in business information systems and analytics (ABISA) book series Online social networks Social sciences Network analysis Data mining Computational linguistics Data Mining (DE-588)4428654-5 gnd rswk-swf Netzwerkanalyse (DE-588)4075298-7 gnd rswk-swf Social Media (DE-588)4639271-3 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Social Media (DE-588)4639271-3 s Netzwerkanalyse (DE-588)4075298-7 s Data Mining (DE-588)4428654-5 s b DE-604 Bansal, Himani edt Shrivastava, Gulshan edt Nguyen, Gia Nhu Sonstige oth Stanciu, Loredana-Mihaela Sonstige oth Erscheint auch als Online-Ausgabe 978-1-5225-5098-3 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030305999&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Social network analytics for contemporary business organizations Online social networks Social sciences Network analysis Data mining Computational linguistics Data Mining (DE-588)4428654-5 gnd Netzwerkanalyse (DE-588)4075298-7 gnd Social Media (DE-588)4639271-3 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4075298-7 (DE-588)4639271-3 (DE-588)4143413-4 |
| title | Social network analytics for contemporary business organizations |
| title_auth | Social network analytics for contemporary business organizations |
| title_exact_search | Social network analytics for contemporary business organizations |
| title_full | Social network analytics for contemporary business organizations Himani Bansal, Guishan Shrivastava, Gia Nhu Nguyen and Loredana-Mihaela Stanciu |
| title_fullStr | Social network analytics for contemporary business organizations Himani Bansal, Guishan Shrivastava, Gia Nhu Nguyen and Loredana-Mihaela Stanciu |
| title_full_unstemmed | Social network analytics for contemporary business organizations Himani Bansal, Guishan Shrivastava, Gia Nhu Nguyen and Loredana-Mihaela Stanciu |
| title_short | Social network analytics for contemporary business organizations |
| title_sort | social network analytics for contemporary business organizations |
| topic | Online social networks Social sciences Network analysis Data mining Computational linguistics Data Mining (DE-588)4428654-5 gnd Netzwerkanalyse (DE-588)4075298-7 gnd Social Media (DE-588)4639271-3 gnd |
| topic_facet | Online social networks Social sciences Network analysis Data mining Computational linguistics Data Mining Netzwerkanalyse Social Media Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030305999&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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