Survey of data mining techniques for social

A Survey of Data Mining Techniques for Social
Network Analysis
Mariam Adedoyin-Olowe1
, Mohamed Medhat Gaber1
and Frederic Stahl2
1
School of Computing Science and Digital Media, Robert Gordon
University
Aberdeen, AB10 7QB, UK
2
School of Systems Engineering, University of Reading
PO Box 225, Whiteknights, Reading, RG6 6AY, UK
Abstract. Social network has gained remarkable attention in the last decade.
Accessing social network sites such as Twitter, Facebook LinkedIn and Google+
through the internet and the web 2.0 technologies has become more affordable.
People are becoming more interested in and relying on social network for
information, news and opinion of other users on diverse subject matters. The heavy
reliance on social network sites causes them to generate massive data characterised
by three computational issues namely; size, noise and dynamism. These issues often
make social network data very complex to analyse manually, resulting in the
pertinent use of computational means of analysing them. Data mining provides a
wide range of techniques for detecting useful knowledge from massive datasets like
trends, patterns and rules [44]. Data mining techniques are used for information
retrieval, statistical modelling and machine learning. These techniques employ data
pre-processing, data analysis, and data interpretation processes in the course of
data analysis. This survey discusses different data mining techniques used in mining
diverse aspects of the social network over decades going from the historical
techniques to the up-to-date models, including our novel technique named TRCM.
All the techniques covered in this survey are listed in the Table.1 including the tools
employed as well as names of their authors.
Keywords: Social Network, Social Network Analysis, Data Mining
Techniques
1. Introduction
Social network is a term used to describe web-based services that allow
individuals to create a public/semi-public profile within a domain such that
they can communicatively connect with other users within the network [22].
Social network has improved on the concept and technology of Web 2.0, by
enabling the formation and exchange of User-Generated Content [46].
Simply put, social network is a graph consisting of nodes and links used to
represent social relations on social network sites [17]. The nodes include
entities and the relationships between them forms the links (as presented in
Fig. 1).
Fig. 1. Social Network showing nodes and links

Social networks are important sources of online interactions and contents
sharing [81], [21], subjectivity [6], assessments [52], approaches [54],
evaluation [48], influences [8], observations [24], feelings [46], opinions
and sentiments expressions [66] borne out in text, reviews, blogs,
discussions, news, remarks, reactions, or some other documents [57]. Before
the advent of social network, the homepages was popularly used in the late
1990s which made it possible for average internet users to share
information. However, the activities on social network in recent times seem
to have transformed the World Wide Web (www) into its intended original
creation. Social network platforms enable rapid information exchange
between users regardless of the location. Many organisations, individuals
and even government of countries now follow the activities on social
network. The network enables big organisations, celebrities, government
official and government bodies to obtain knowledge on how their audience
reacts to postings that concerns them out of the enormous data generated on
social network (as shown in Fig. 2). The network permits the effective
collection of large-scale data which gives rise to major computational
challenges. However, the application of efficient data mining techniques has
made it possible for users to discover valuable, accurate and useful
knowledge from social network data.
Data mining techniques have been found to be capable of handling the
three dominant disputes with social network data namely; size, noise and
dynamism. The voluminous nature of social network datasets require
automated information processing for analysing it within a reasonable time.
Interestingly, data mining techniques also require huge data sets to mine
remarkable patterns from data; social network sites appear to be perfect sites
to mine with data mining tools [27]. This forms an enabling factor for
advanced search results in search engines and also helps in better
understanding of social data for research and organizational functions [4].
Data mining tools surveyed in this paper ranges from unsupervised, semi-
supervised to supervised learning. A table itemizing the techniques covered
in this paper is presented in section 7.
The rest of the survey is organised as follows. Section 2 examines the
social network background. Section 3 listed research issues on social
network analysis. Section 4 discusses some of the Graph Theoretic tools
used for social network analysis. Section 5 gives an overview of tools used
to analyse opinions conveyed on social network while Section 6 presents
some of the sentiment analysis techniques used on social network. Section 7
describes some unsupervised classification techniques employed in social
network analysis. Section 8 presents topic detection and tracking tools used
for social network analysis. The survey is concluded in Section 9 by stating
the direction for future work.
2. Social Network Background
During the last decade social network have become not only popular but
also affordable and universally-acclaimed communication means that has
thrived in making the world a global village. Social network sites are
commonly known for information dissemination, personal activities posting,
product reviews, online pictures sharing, professional profiling,
advertisements and opinion/sentiment expression. News alerts, breaking
news, political debates and government policy are also posted and analysed
on social network sites. It is observed that more people are becoming
interested in and relying on the social network for information in real time.
Users sometimes make decisions based on information posted by unfamiliar
individuals on social network [66] increasing the degree of reliance on the
credibility of these sites. Social network has succeeded in transforming the
way different entities source and retrieve valuable information irrespective
of their location. Social network has also given users the privilege to give
opinions with very little or no restriction.

Fig.2. Estimated Data Generated on Social Network Sites Every Minute
Thumbnail image courtesy of iStockphoto, loops7
http://mashable.com/2012/06/22/data-created-every-minute/
2.1 Social Network – Power to the Users
Social sites have undoubtedly bestowed unimaginable privilege on their
users to access readily available never-ending uncensored information.
Twitter, for example, permits its users to post events in real time way ahead
the broadcast of such events on traditional news media. Also, social network
allow users to express their views, be it positive or negative [4].
Organizations are now conscious of the significance of consumers’ opinions
posted on social network sites to the patronage of their products or services
and the overall success of their organisations. On the other hand, important
personalities such as celebrities and government officials are being
conscious of how they are perceived on social network. These entities
follow the activities on social network to keep abreast with how their
audience reacts to issues that concerns them [20], [23], [40]. Considering the
enormous volume of data being generated on social network, it is imperative
to find a computational means of filtering, categorising, classifying and
analysing the social network contents.
3. Research Issues on Social Network Analysis
A number of research issues and challenges facing the realisation of
utilising data mining techniques in social network analysis could be
identified as follows:
● Linkage-based and Structural Analysis – This is an analysis of
the linkage behaviour of the social network so as to ascertain
relevant nodes, links, communities and imminent areas of the
network - Aggarwal, 2011.
● Dynamic Analysis and Static Analysis – Static analysis such as in
bibliographic networks is presumed to be easier to carry out than
those in streaming networks. In static analysis, it is presumed that
social network changes gradually over time and analysis on the
entire network can be done in batch mode. Conversely, dynamic
analysis of streaming networks like Facebook and YouTube are
very difficult to carry out. Data on these networks are generated at
high speed and capacity. Dynamic analysis of these networks are
often in the area of interactions between entities - Papadopoulos et

al, (2012), temporal events on social networks Adedoyin-Olowe et
al (2013); Becker et al (2011) and evolving communities -
Fortunato, (2010).
Having presented some of the research issues and challenges in social
network analysis, the following sections and sub-sections present the
overview of different data mining approaches used in analysing social
network data.
4 Graph Theoretic
Graph theory is probably the main method in social network analysis in
the early history of the social network concept. The approach is applied to
social network analysis in order to determine important features of the
network such as the nodes and links (for example influencers and the
followers). Influencers on social network have been identified as users that
have impact on the activities or opinion of other users by way of
followership or influence on decision made by other users on the network
(as presented in Fig.3). Graph theory has proved to be very effective on
large-scale datasets (such as social network data). This is because it is
capable of bye-passing the building of an actual visual representation of the
data to run directly on data matrices [76]. In [19] centrality measure was
used to inspect the representation of power and influence that forms clusters
and cohesiveness [16] on social network. The authors of [34] employed
parameterized centrality metric approach to study the network structure and
to rank nodes connectivity. Their work formed an extension of a-centrality
approach which measures the number of alleviated paths that exist among
nodes.
Fig 3. Graph Theoretic on Social Network
4.1 Community Detection Using Hierarchical Clustering
A community is a smaller compressed group within a larger network (as
shown in Fig.4). Community formation is known to be one of the important
characteristics of social network sites. Users with similar interest form
communities on social network thereby displaying strong sectional
structure. Communities on social networks, like any other communities in
the real world, are very complex in nature and difficult to detect. Applying
the appropriate tools in detecting and understanding the behaviour of
network communities is crucial as this can be used to model the dynamism
of the domain they belong [4]. Different authors have applied diverse
clustering techniques to detect communities on social network [35]; [32];
68], with hierarchical clustering being mostly used [62]. This technique is
a combination of many techniques used to group nodes in the network to
reveal strength of individual groups which is then used to distribute the
network into communities. Vertex clustering belongs to hierarchical
clustering methods, graph vertices can be resolved by adding it in a vector

space so that pairwise length between vertices can be measured. Structural
equivalence measures of hierarchical clustering concentrate on number of
common network connections shared by two nodes. Two people on social
network with several mutual friends are more likely to be closer than two
people with fewer mutual friends on the network. Users in the same social
network community often recommend items and services to one another
based on the experience on the items or services involved. This is known as
recommender system as explains next in section 4.2.
4.2 Recommender System in Social Network Community
Based on the mutuality between nodes in social network groups,
collaborative filtering (CF) technique, which forms one of the three classes
of the recommender system (RS), can be used to exploit the association
among users [56]. Items can be recommended to a user based on the rating
of his mutual connection. Where CF’s main downside is that of data
sparsity, content-based (another RS method) explore the structures of the
data to produce recommendations. However, the hybrid approaches usually
suggest recommendations by combining CF and content-based
recommendations. The experiment in [18] proposed a hybrid approach
named EntreeC, a system that pools knowledge-based RS and CF to
recommend restaurants. The work in [69] improved on CF algorithm by
using a greedy implementation of hierarchical agglomerative clustering to
suggest forthcoming conferences or journals in which researchers
(especially in computer science) can submit their work.
Fig. 4 Social Network Community Structure
4.3 Semantic Web of Social Network
The Semantic Web platform makes knowledge sharing and re-use
possible over different applications and community edges. Discovering the
evolvement of Semantic Web (SW) enhances the knowledge of the
prominence of Semantic Web Community and envisages the synthesis of
the Semantic Web. The work in [92] employed Friend of a Friend (FOAF)
to explore how local and global community level groups develop and evolve
in large-scale social networks on the Semantic Web. The study revealed the
evolution outlines of social structures and forecasts future drift. Likewise
[74] application model of Semantic Web-based Social Network Analysis
Model creates the ontological field library of social network analysis
combined with the conventional outline of the semantic web to attain

intelligent retrieval of the Web services. Furthermore, VoyeurServer [61]
improved on the open-source Web-Harvest framework for the collection of
online social network data in order to study structures of trust enhancement
and of online scientific association. Semantic Web is a relatively new area in
social network analysis and research in the field is still evolving.
5 Opinion Analysis on Social Network
According to Technorati, about 75,000 new blogs and 1.2 million new
posts giving opinion on products and services are generated every day [50].
Also massive data generated every minute on common social network sites
(as presented in Fig. 2) are laden with opinion of users as regards diverse
subject ranging from personal to global issues [80]. Users’ opinions on
social network sites can be referred to as discovery and recognition of
positive or negative expression on diverse subject matters of interest. These
opinions are often convincing and their indicators can be used as motivation
when making choices and decisions on patronage of certain products and
services or even endorsement of political candidate during elections [47],
[67]. Even though online opinions can be discovered using traditional
methods, this form is conversely inadequate considering the large volume of
information generated on social network sites. This fact underscores the
relevance of data mining techniques in mining opinion expressed on social
network site.
Various methods have been developed to analyse the opinion arising
from products, services, events or personality review on social network [36].
Data mining tools already used for opinion and sentiment analysis include
collections of simple counting methods to machine learning. Categorizing
opinion-based text using binary distinction of positive against negative [39],
[29], [64], [85], is found to be insufficient when ranking items in terms of
recommendation or comparison of several reviewers’ opinions [65] (e.g.,
using actors starred in two different films to decide which of them to see at
the cinema). Determining players from documents on social network has
also become valuable as influential actors are considered as variables in the
documents [91] when applying data mining techniques on social network.
The idea of co-occurrence can also be seen as viable information [34].
Data mining techniques used for opinion mining on social network are
discussed in the next section of this survey.
5.1 Aspect-Based/Feature-Based Opinion Mining
Aspect-based also known as feature-based analysis is the process of
mining the area of entity customers has reviewed [41]. This is because not
all aspects/features of an entity are often reviewed by customers. It is then
necessary to summarise the aspects reviewed to determine the polarity of the
overall review whether they are positive or negative. Sentiments expressed
on some entities are easier to analyse than others, one of the reason being
that some reviews are ambiguous.
According to [57] aspect-based opinion problem lies more in blogs and
forum discussions than in product or service reviews. The aspect/entity
(which may be a computer device) reviewed is either ‘thumb up’ or ‘thumb
down’, thumb up being positive review while thumb down means negative
review. Conversely, in blogs and forum discussions both aspects and entity
are not recognized and there are high levels of insignificant data which
constitute noise. It is therefore necessary to identify opinion sentences in
each review to determine if indeed each opinion sentence is positive or
negative [41]. Opinion sentences can be used to summarize aspect-based
opinion which enhances the overall mining of product or service review.
An opinion holder expresses either positive or negative opinion [13],
[51], [89] on an entity or a portion of it when giving a regular opinion and

nothing else [41]. However, [53] put necessity on differentiating the two
assignments of finding out neutral from non-neutral sentiment, and also
positive and negative sentiment. This is believed to greatly increase the
correctness of computerised structures.
5.2 Homophily Clustering in Opinion Formation
Opinion of influencers on social network is based largely on their
personal views and cannot be hold as absolute fact. However, their opinions
are capable of affecting the decisions of other users on diverse subject
matters. Opinions of influential users on Social network often count,
resulting in opinion formation evolvement. Clustering technique of data
mining can be utilised to model opinion formation by way of assessing the
affected nodes and unaffected nodes. Users that depict the same opinion are
linked under the same nodes and those with opposing opinion are linked in
other nodes (as shown in Fig.5). This concept is referred to as homophily in
social network [59]. Homophily can also be demonstrated using other
criteria such as race and gender [42].
Fig. 5. Homophily Showing Influencer and Followers’ Opinion in
Social Network
Behaviour of participants in each node is subject to adjustments in
awareness of the behaviour of participants in other nodes [47]. Opinion
formation starts from the initial stage where bulk of participants pays no
attention to recourse action on significant issue at this stage. This is so
because they do not consider the action viable. When cogent information is
introduced opinion is cascaded and participants begin to make either
positive or negative decisions. At this stage the decision of influential
participants who are either efficient in the field or in communication skill
attracts the followership of the minority. The initial stage (as presented Fig.
6) is then transformed to alert stage (in Fig. 7). The percolate stage sets in
when the minority are able to form a different opinion based on other
agents’ behaviour and introduction of new information. It is worthy of note
that opinion of social network users is oftentimes mix with sentiment;
sentiment analysis on social network is discussed next in section.6.

Fig.6. Initial stage of Opinion Formation
Fig. 7 Alert State of Opinion Formation
5.3 Opinion Definition and Opinion Summarization
Extensive information gives rise to challenge of automatic summarization.
Opinion definition and opinion summarization are essential techniques for
recognizing opinion. Opinion definition can be located in a text, sentence or
topic in a document; it can also reside in the entire document. Opinion
summarization sums up different opinions aired on piece of writing by
analysing the sentiment polarities, degree and the associated occurrences.
Authors in [55] used Support Vector Machine (SVM) with linear kernel to
learn the polarity of neutral examples in documents. Their results propose
that polarity complications can be adequately handled as three class
complications using pairwise coupling while merging outcomes in
remarkable ways. Opinion extraction is vital for summarization and
subsequent tracking. Texts, topics and documents are searched to extract
opinionated part. It is necessary of summarise opinion because not all
opinion expressed in a document are expected to be of importance to issue
under consideration. Opinion summarization is quiet useful to businesses
and government alike, as it helps in improving policies and products
respectively [29], [60]. It also helps in taking care of information excess
[87].

5.4 Opinion Extraction
Sentiment analysis deals with establishment and classification of
subjective information present in a material [86]. This might not necessarily
be fact-based as people have different feelings toward the same product,
service, topic, event or person. Opinion extraction is necessary in order to
target the exact part of the document where the real opinion is expressed.
Opinion from an individual in a specialised subject may not count except if
the individual is an authority in the field of the subject matter. Nevertheless,
opinion from several entities necessitates both opinion extraction and
summarization [57].
In opinion extraction, the more the number of people that give their
opinion on a particular subject, the more important that portion might be
worth extracting. Opinion can aim at a particular article while on the other
hand can compare two or more articles. The formal is a regular opinion
while the latter is comparative [43]. Opinion extraction identifies subjective
sentences with sentimental classification of either positive or negative.
6 Sentiment Analysis on Social Network
Sentiment analysis research has its roots in papers published by [28] and
[83] where they analysed market sentiment. The concept later gained more
ground the year after where authors like [64] and [85] have reported their
findings. Sentiment analysis can be referred to as discovery and recognition
of positive or negative expression of opinion by people on diverse subject
matters of interest. Opinions expressed by social network users are often
convincing and these indicators can be used to form the basis of choices and
decisions made by people on patronage of certain products and services or
endorsement of political candidate during elections [47], [67].
It is worthy of note that the enormous opinions of several millions of
social network users are overwhelming, ranging from very important ones to
mere assertions (e.g. “The phone does not come in my favourite colour,
therefore it is a waste of money”). Consequentially it has become necessary
to analyse sentiment expressed on social network with data mining
techniques in order to generate a meaningful frameworks that can be used as
decision support tools. Diverse algorithms are employed to ascertain
sentiment that matters to a topic, text, document or personality under
review. The purpose of sentiment analysis on social network is to recognize
potential drift in the society as it concerns the attitudes, observations, and
the expectations of stakeholder or the populace. This recognition enables the
entities concern to take prompt actions by making necessary decisions. It is
important to translate sentiment expressed to useful knowledge by way of
mining and analysis.
Having given an overview of sentiment analysis on social network, an
overview of some of the data mining tools used for sentiment analysis on
social network are discussed in subsequent sections of the survey.
6.1 Sentiment Orientation (SO)
Widespread products are likely to attract thousands of reviews and this
may make it difficult for prospective buyers to track usable reviews that
may assist in making decision. On the other hand sellers make use of
Sentiment Orientation (SO) for their rating standard in other to safeguard
irrelevant or misleading reviews present to reviewers the 5-star scale rating
with five signifying best rated while one signifies poor rating. In [49] SO
was used to improve the performance of mood classification. Livejournal
blog corpus dataset was used to train and evaluate the method used. The
experiment presented a modular proficient hierarchical classification

technique easily implemented together with SO attributes and machine
learning techniques. The initial result of classification accuracy however
recorded slightly above the baseline. An incorporation of flexible hierarchy-
based mood approach to mood classification discovers that attributes that
points to accurate classification of mood expression can be retrieved from
the various dense blog corpus domain.
6.2 Product Ratings and Reviews
The reliance on the internet (especially social network sites) for information
when making choices about products or services has increased the necessity
of researching into the electronic-word-of-mouth. Products (services) ratings
and reviewing often contains sentiment expressions [33]; [79], an item can
be rated based on the mood of the reviewer at the time [15]. Social network
sites such as Epinions and Ciao allow users to establish a trust network
among them showing who to trust in offering product reviews and ratings.
Most online stores equally afford their customers the opportunity to either
rate/review product or service they purchased. This process enables
prospective customers to have access to first-hand information about these
products/services before making purchase. Poorly rated or reviewed
products/services tend to attract very low patronage or no patronage at all.
Data mining tools are used to analyse the concept of products ratings and
reviews on social network. The experiments in [7] proposed an advanced
matrix factorisation method capable of increasing rating predictions and
estimate accurate strengths of trust associations within the same period.
Their work suggests that even though general users who trust others in the
same network tend to have similar ratings over time (homophily and social
influence), this does not implies similar preferences. Furthermore, [78]
proposed a simplified context that exploits multi-modal social networks that
provide item recommendations in social rating networks (SRNs). Using their
Social-Union method which associates similarity matrices originated from
heterogeneous (unipartite and bipartite) unequivocal or implied SRNs, they
concluded that social-union out-performed existing renowned item
recommendation systems.
6.3 Reviews and Ratings (RnR) Architecture (Rahayu, 2010)
RnR is a conceptual architecture created as an interactive structure. It is
user input oriented that develops relative new reviews. The user supplies the
name of product or service whose performance has been earlier reviewed
online by customers. This systems checks through the corpus of already
reviewed to find out if it is stored in the local cache of the architecture for
latest reviews. If the supplied data is found to be recent, it is subsequently
used. If it is found to be obsolete, crawling is done on secondary site such as
TripAdvisor.com and Expedia.com.au. The data retrieved on these sites are
then locally built to discover the required justification. RnR architecture
produced complete remarks of product and service (under review) within a
time line by utilising temporal dimension analysis with scatter plot and
linear regression. Tagging of few words was used for free accessible
domain ontology for feature identification. This is because tagging POS of
each word in whole reviews and opinion word identification process could
be time consuming and computationally expensive even though it produces
high accuracy. The use of neighbour word (words around the feature
occurrences) also aids the pruning down of computational overhead.

6.4 Aspect Rating Analysis
Aspect-rating is numerical evaluations in relation to the aspect pointing
to the level of satisfaction portrayed in the comments gathered toward this
aspect and the aspect rating. It makes use of diminutive phrases and their
modifiers [57], for example ‘good product, excellent price’. Each aspect is
extracted and collated using Probabilistic latent semantic analysis (pLSA). It
can be used in place of structure of the phrase. The already known complete
post is exploited to ascertain the aspect rating. Aspect cluster are words that
communally stands for an aspect that users are concerned in and would
comment on.
Latent Aspect Rating Analysis (LARA) approach attempts to analyse
opinion borne by different reviewers by doing a text mining at the point of
topical aspect. This enables the determinance of every reviewer’s latent
score on each aspects and the relevant influence on them when arriving at
an affirmative conclusion. The revelation of the latent scores on different
aspects can instantly sustain aspect-base opinion summarization. The aspect
influences are proportional to analysing score performance of reviewers.
The fusion latent scores and aspect influences is capable of sustaining
personalised aspect-level scoring of entities using just those reviews
originated from reviewers with comparable aspect influences to those
considered by a particular user [87]. An aspect-based summarization make
use of set of user reviews of a subject as input and creates a set of important
aspects taking into consideration the combined sentiment of each aspect and
supporting textual indication[82].
Sentiment analysis tools used for social network analysis commonly
utilize sentimental words often compiled into sentiment lexicon (also know
as sentiment dictionary). Sentiment lexicon is briefly discussed next in
section 6.5.
6.5 Sentiment Lexicon
Sentiment Lexicon is a dictionary of sentimental words reviewers often
used in their expression. Sentiment lexicon is list of the common words that
enhances data mining techniques when used mining sentiment in document.
Different corpus of sentiment lexicon can be created for variety of subject
matters. For instance sentimental words used in sport are often different
from those used in politics. Expanding the occurrence of sentiment lexicon
helps to focus more on analysing topic-specific occurrence, but with the use
of high manpower [36]. Lexicon-based approaches require parsing to work
on simple, comparative, compound, conditional sentences and questions
[30].
Sentiment lexicon can be expanded by use of synonyms. Nonetheless,
lexicon expansion through the use of synonyms has a drawback of the
wording loosing it primary meaning after a few recapitulation. Sentiment
lexicon can also be enhanced by ‘throwing away’ neutral words that depicts
neither positive nor negative expression. Neutral expression is common
especially in products ratings and reviews.
7 Unsupervised Classification of Social Network Data
A straightforward unsupervised learning algorithm can be used to rate a
review as ‘thumbs up’ or ‘thumbs down’ [85]. This can be by way of
digging out phrases that include adjective or adverbs (part of speech
tagging) [75]. The semantic orientation of every phrase can be approximated
using PMI-IR [120] and then classify the review using the average
semantic orientation of the phrase. Cogency of title, body and comments
generated from blog post has also been used in clustering similar blogs into
significant groups. In this case keywords played very important role which
may be multifaceted and bare [3]. EM-based and constrained-LDA were

utilized to cluster aspect phrases into aspect categories [90]. In [12] two
unsupervised frameworks based on link structure of the Web pages, and
Agglomerative/Conglomerative Double Clustering (A/CDC) was used to
find group of individuals on the web. The result proves to be more accurate
than those obtained by traditional agglomerative clustering by more than
20% while achieving over 80% F-measure.
Other unsupervised learning used in sentiment analysis in products rating
and reviews include POS (Part of Speech) tagging. In POS adjectives are
tagged to display positive and negatives ones. Sentiment polarity is the
binary classification of an opinionated document into a largely positive and
negative opinion [67]. In review this is commonly termed with the ‘thumps
up’ and ‘thumps down’ expressions as mentions earlier. The polarity of
positive against negative is weighed to give an overall analysis of sentiment
expressed on issue under review. Bootstrapping also forms part of the
unsupervised approaches. It utilizes obtainable primary classifier to make
labelled data which a supervised process can build upon [45], [73], [89].
Semantic orientation is also an unsupervised approaches currently used for
sentiment analysis on social network. It attaches different meaning to a
single word – synonym. This could either be positive or negative (for
example ‘the party is bad’ may in actual fact mean the party is fun).
Direction and intensity of words used can determine the semantic
orientation of the opinion expressed [14].Semi-supervised and supervised
classification are more structured techniques as discussed next in Sections
7.1 and 7.2.
7.1 Semi-supervised Classification
Semi-supervised learning is a goal-targeted activity but unlike
unsupervised; it can be specifically evaluated. Authors of [31] worked on a
mini training set of seed in positive and negative expressions selected for
training a term classifier. Synonym and antonym comparatives were added
to the seed sets in an online dictionary. The approach was meant to produce
the extended sets P’ and N’ that makes up the training sets. Other learners
were employed and a binary classifier was built using every glosses in the
dictionary for both term in P’∪ N’ and translating them to a vector. Their
approach discovers the origin of information which they reported was
missing in earlier techniques used for the task. Semi-supervised lexical
classification proposed by [77] integrated lexical knowledge into supervised
learning and spread the approach to comprise unlabelled data. Cluster
assumption was engaged by grouping together two documents with the same
cluster basically supporting the positive - negative sentiment words as
sentiment documents. It was noted that the sentiment polarity of document
decides the polarity of word and vice versa.
In [72] semi-supervised learning uses polarity detection as semi-
supervised label propagation problem in graphs. Each node representing
words whose polarity is to be discovered. The results shows label
propagation progresses outstandingly above the baseline and other semi-
supervised techniques like Mincuts and Randomized Mincuts. The work of
[38] compared graph-based semi-supervised learning with regression and
[65] proposed metric labelling which runs SVM regression as the original
label preference function comparable to similarity measure. Their result
shows that the graph-based semi-supervised learning (SSL) algorithm as per
PSP (positive-sentence-percentage) comparison (SSL+PSP) proved to
perform well.

7.2 Supervised Classification
While clustering techniques are used where basis of data is established
but data pattern is unknown [4], classification techniques are supervised
learning techniques used where the data organisation is already identified. It
is worthy of mention that understanding the problem to be solved and opting
for the right data mining tool is very essential when using data mining
techniques to solve social network issues. Pre-processing and considering
privacy rights of individual (as mentioned under research issues of this
paper) should also be taken into account. Nonetheless, since social media is
a dynamic platform, impact of time can only be rational in the issue of topic
recognition, but not substantial in the case of network enlargement, group
behaviour/ influence or marketing. This is because this attributes are bound
to change from time to time. Information updates in some Social network
such as twitters and Facebook present Application Programmers Interfaces
(APIs) that makes it possible for crawler, which gather new information in
the site, to store the information for later usage and update.
In [85] a supervised learning algorithm used the combination of multiple
bases of facts to label couple of adjectives having similar or dissimilar
semantic orientations. The algorithm resulted in a graph with nodes and
links which represents adjectives and similarity (or dissimilarity) of
semantic orientation respectively.
8 Topic Detection and Tracking on Social Network
Topic Detection and Tracking (TDT) on social network employs
different techniques for discovering the emergent of new topics (or events)
and for tracking their subsequent evolvements over a period of time. TDT is
receiving high level of attention recently. Many researchers and authors are
conducting experiments on TDT on social network sites, especially on
Twitter [1]; [37]; [9]; [10]; [63]; [70]; [58]. In [25] support vector machine
(SVM) was found to be efficient in training Twitter hashtags metadata when
predicting the political alignment of twitter users. Authors of [9] used an
incremental online clustering algorithm to cluster a stream of Twitter
messages in real time. They trained a Naïve Bayes-Text classifier to
distinguish between fastest-growing real-world events contents and non-
events contents on Twitter. The performance of the training set shows the
precision of all classifier computed in 10-fold cross-validation. The
experiments in [11] used a range of query-building approaches to
automatically enhance user-contributed information for planned events with
robustly generated Twitter contents. Their approach used browser plug-in
script and a customizable web interface to identify relevant Twitter content
for planned events.
The experiments in [5] proposed a combination of six techniques
namely; LDA (Latent Dirichlet Allocation), Doc-p (Document-Pivot Topic
Detection, GFeat-p (Graph-based Feature-Pivot Topic Detection), FPM
(Frequent Pattern Mining), SFPM (Soft Frequent Pattern Mining) and
BNgram for real-world event detection on Twitter network. The techniques
were verified on tweets relating to three major events (English FA Cup
Finals, US Super Tuesday Primaries and US Elections 2012) with variations
in time scale and topic mix level. The algorithms revealed that dataset pre-
processing and sampling process affects the quality of topic retrieved.
Conversely, the algorithms performed optimally on the three datasets
considered. Similarly, [70] proposed an algorithm for detecting and tracking
breaking news in Twitter. The application named “Hotstream” was built to
afford its users the opportunity of detecting and tracking breaking news
from Twitter timeline. Authors of [63 proposed a state-of-the-art First Story
Detection (FSD) technique to detect predictable and unpredictable events
using real-time indication from Wikipedia and Twitter data streams. The
result of the experiments recorded about 2-hour delay for Wikipedia in real-

world events. Authors in [88] used EDCoW (Event Detection with
Clustering of Wavelet-based Signals) to cluster words to form events with a
modularity-based graph partitioning method. On the other hand [26]
employed lightweight event detection using wavelet signal analysis of
hashtags occurrences in Twitter public stream. The experiments used Latent
Dirichlet Allocation topic inference model based on Gibbs Sampling. The
outcome of the experiments shows that peak detection using Continuous
Wavelet Transformation realized impressive outcomes in the ascertaining
abrupt increases on the mention of specific hashtags. In [1] the abruptness in
hashtags usage is labelled unexpected rule evolvement which is discussed
under TRCM in Section 8.1.
8.1 TRCM for TDT
Twitter as a social network and hashtags as tweet labels can be analysed
in order to detect changes in event patterns (TDT) using Association Rules
(ARs). Twitter data can be used to analyse patterns associated with events by
detecting the dynamics of the tweets. Association Rule Mining (ARM) can
find the probability of co- existence of tweets’ hashtags. Firstly, in [1] ARM
was used to analyse tweets on the same topic over consecutive time periods
t and t+1. Rule Matching (RM) was later employed to detected changes in
patterns such as ‘emerging (EM)’, ‘unexpected consequent (UnxCs)’ and
‘unexpected conditional (UnxCn)’, ‘new (N)’ and ‘dead (D)’ rules in
tweets. This is obtained by setting a user-defined Rule Matching Threshold
(RMT) to match rules in tweets at time t with those in tweets at t + 1 in order
to ascertain rules that fall into the different patterns (as presented in Fig.8).
The proposed methodology was coined TRCM (Transaction-based Rule
Change Mining). All the detected rules in TRCM were linked to real life
events and news reports.
Fig.8. TRCM Rules
In [37] Subsequently, TRCM was utilized to discover the rule trend of
tweets’ hashtags over a consecutive period. Time Frame Windows (TFWs)
was created (as shown in Fig. 9) to describe different rule evolvement
patterns which can be applied to evolvements of news and events in reality.
This concept of TRCM was named TRCM-RTI (Transaction-based Rule
Change Mining-Rule Type Identification).

Fig.9. Different Sequences of Time Frame Windows (TFWs)
Time frame window also serve as a means of calculating the lifespan
of specific hashtags on Twitter. Using the experimental study result in [37],
it was substantiated that the lifespan of tweets’ hashtags can be related to
evolvements of news and events in reality. The TRCM techniques [1] and
[37] can be said to be the first time ARM was used to mine Twitter data.
This therefore opens up the area for further research as ARM can be fine-
tuned to be used to mine data on other social networks sites for information
retrieval and knowledge acquisition.
Finally different approaches covered in this survey are listed in
Table.1.
9 Conclusion and Future Work
Different data mining techniques have been used in social network
analysis as covered in this survey. The techniques range from unsupervised
to semi-supervised and supervised learning methods. So far different levels
of successes have being achieved either with solitary or combined
techniques. The outcome of the experiments conducted on social network
analysis is believed to have shed more light on the structure and activities of
social network. The diverse experimental results have also confirmed the
relevance of data mining techniques in retrieving valuable information and
contents from huge data generated on social network. Future survey will
tend to investigate novel state-of-the-art data mining techniques for social
network analysis. The survey will compare similar data mining tools and
recommend the most suitable tool(s) for the dataset to be analysed.
Different data mining techniques covered in this survey are listed in
Table.1. The table also contains the approaches employed, the experimental
results and the dates and authors of the approaches.

Table.1 List of Data mining Techniques currently in Used in Social Network
Analysis.
Approach Tools Experiments Authors/dates
Graph
Theoretic
Centrality measure Inspects
representation of
power and influence
that forms clusters
and cohesiveness.
Burts (2005)
Borgatti & Everett
(2006)
Parameterized
centrality metric
Studies the network
structure and to rank
nodes connectivity.
Ghosh & Leman
(2011)
a-centrality Measures the
number of alleviated
paths that exist
among nodes.
Bonacich & Lloyd
(2001)
Community
Detection
(hierarchical
clustering)
Vertex clustering Measures pairwise
length between
vertices.
Papadopoulos et al
(2012)
Structural
equivalence
measures
Detects friendship
structure on social
network based on
shared behaviour.
Fletcher et al
(2011)
Random-walk-
based similarity
-walktrap
Detects a
hierarchical structure
of small
communities
Pons and Latapy,
(2005)
Recommender
System
CF (Collaborative
filtering)
Exploits association
among users by way
of item
recommendation.
Liu & HJ Lee
(2010)
Content-based Explores the
structures of the data
to produce
recommendations.
Adomavicius &
Tuzhilin, (2005)
Hybrid approach -
EntreeC
Suggest
recommendations by
combining CF and
content-based
recommendations.
Burke (2002)

Hierarchical
agglomerative
clustering
Use to suggest
forthcoming
conferences or
journals in which
researchers.
Pham et al (2011)
Semantic Web Friend of a Friend
(FOAF)
Used to explore how
local and global
community level
groups develop and
evolve in large-scale
social networks on
the Semantic Web.
Zhou et al (2011)
Semantic Web-
based Social
Network Analysis
Model
Combined with
conventional outline
of the semantic web
to create the
ontological field
library of social
network analysis in
order to attain
intelligent retrieval
of the Web services.
Ruan et al (2014)
VoyeurServer Improved on the
open-source Web-
Harvest framework
for the collection of
online social
network data for
studying structures
of trust enhancement
and online scientific
association.
Murthy et al
(2013)
Opinion
Analysis
Aspect-
Based/Feature-
Based
To identify positive
or negative opinion
sentences
in product reviews.
Hu & Liu (2004)
Opinion
Formation
Homophily
Clustering
Used to link same
opinion under the
same nodes.
Lynn Smith-Lovin
&
Cook, (2001)
Jackson M, 2010
Opinion
Definition and
Opinion
Summarization
Support Vector
Machine
(SVM/linear kernel
Used to learn the
polarity of neutral
examples in
documents.
Ku et al (2006)
Sentiment
Orientation
(SO)
hierarchical
classification
technique
Used to improve the
performance of
mood classification.
Keshtkar, &
Inkpen (2009)
Product Ratings
and Reviews
Matrix factorisation
method
Used to increase
rating predictions
and estimate
accurate strengths of
trust associations
Au Yeung and
Iwata (2011)

within the same
period.
Social-Union
method
Used to exploit
multi-modal social
networks that
provide item
recommendations in
social rating
networks (SRNs).
Symeonidis et al
(2011)
RnR
Reviews and
Ratings
It employs user-
input- oriented-
system to develop
relative new
reviews.
Rahayu, 2010
Aspect Rating
Analysis
Latent Aspect
Rating Analysis
(LARA)
Used to determine
every reviewer’s
latent score on each
aspects and the
relevant influence on
users when making
final decision.
Wang et al (2010)
Unsupervised
Classification
part of speech
tagging
Used to rate a
review as ‘thumbs
up’ or ‘thumbs
down’.
Santorini (1995)
PMI-IR/ average
semantic
orientation of
phrase.
Used to approximate
semantic orientation
of phrases.
Turney (2001)
EM-based &
constrained-LDA
Used to cluster
aspect phrases into
aspect categories.
Zhai et al, (2011)
Bootstrapping It utilizes obtainable
primary classifier to
make labelled data
which a supervised
process can build
upon.
Kaji &
Kitsuregawa
(2006), Riloff et al
(2003), Wiebe &
Riloff (2005)
Semi-
supervised
Classification
Semi-supervised
lexical
classification
Used to integrate
lexical knowledge
into supervised
learning.
Sindhwani &
Melville (2008)
Polarity detection Use to solve label
propagation in
graphs.
Rao
&Ravichandran
(2009)
Metric
labelling/SVM
regression
Used to compare
similarity measure.
Pang & Lee
(2005)

Supervised
Classification
Multiple facts base Used to create graph
with nodes and links
which represents
adjectives and
similarity.
Turney (2002)
Topic Detection
and Tracking
(TDT)
SVM
(Support vector
machine)
Used to train Twitter
hashtags metadata to
predict the political
alignment of twitter
users.
Conover et al,
(2011)
incremental online
clustering/ Naïve
Bayes-Text
classifier
Used to distinguish
between fastest
growing real world
events contents and
non-events contents
on Twitter.
Becker et al
(2011)
browser plug-in
script &
customizable web
interface
Used to identify
relevant Twitter
content for planned
events.
Becker et al
(2011b)
LDA (Latent
Dirichlet
Allocation), Doc-p
(Document-Pivot
Topic Detection,
GFeat-p (Graph-
based Feature-Pivot
Topic Detection),
FPM (Frequent
Pattern Mining),
SFPM (Soft
Frequent Pattern
Mining) and
BNgram
Used to detect real
world events on
Twitter.
Aiello et al (2013)
Hotstream Used for detecting
and tracking
breaking news in
Twitter.
Phuvipadawat &
Murata (2010)
FSD (First Story
Detection)
Used to detect
predictable and
unpredictable events
using real-time
indication from
Wikipedia and
Twitter data streams.
Osborne et al
(2012)
EDCoW (Event
Detection with
Clustering of
Wavelet-based
Signals)
Used to cluster
words to form events
with a modularity-
based graph
partitioning method.
Weng and Lee
(2011)

Latent Dirichlet
Allocation topic
inference model
Used to ascertain
sudden increases on
the mention of
specific hashtags.
Cordeiro (2012)
TRCM
(Transaction-based
Rule Change
Mining).
Used to link the
evolvement of
Association Rules
present in tweets’
hashtags with
evolvements of real
life news/ events.
Adedoyin-Olowe
et al (2013)
References
1. Adedoyin-Olowe, M., Gaber, M., Stahl, F.: A Methodology for
Temporal Analysis of Evolving Concepts in Twitter. In:
Proceedings of the 2013 ICAISC, International Conference on
Artificial Intelligence and Soft Computing. 2013.
2. Adomavicius, G., Tuzhilin, A.: Toward the next generation of
recommender systems: A survey of the state-of-the-art and possible
extensions. Knowledge and Data Engineering, IEEE Transactions
on, 17(6), 734-749, 2005.
3. Aggarwal, N., Liu, H.: Blogosphere: Research Issues, Tools,
Applications. ACM SIGKDD Explorations. Vol. 10, issue 1, 20,
2008.
4. Aggarwal, C.: An introduction to social network data analytics.
Springer US, 2011.
5. Aiello, L. M., Petkos, G., Martin, C. et al.: Sensing trending
topics in Twitter. 2013.
6. Asur, S., and Huberman, B.: "Predicting the future with social
network." Web Intelligence and Intelligent Agent Technology (WI-
IAT), 2010 IEEE/WIC/ACM International Conference on. Vol. 1.
IEEE, 2010.
7. Au Yeung, C. M., and Iwata, T.: Strength of social influence in
trust networks in product review sites. In Proceedings of the fourth
ACM international conference on Web search and data mining (pp.
495-504). ACM, 2011.
8. Bakshy, E., Hofman, J. M., Mason, W. A., Watts, D. J.:
Identifying influencers on twitter. In Fourth ACM International
Conference on Web Seach and Data Mining (WSDM), 2011.
9. Becker, H., Naaman, M., Gravano, L.: Beyond Trending Topics:
Real-World Event Identification on Twitter. ICWSM, 11, 438-441,
2011.
10. Becker, H., Chen, F., Iter, D., Naaman, M., Gravano, L.:
Automatic Identification and Presentation of Twitter Content for
Planned Events. In ICWSM, 2011.
11. Becker, H., Iter, D., Naaman, M., Gravano, L.: Identifying
content for planned events across social media sites. In
Proceedings of the fifth ACM international conference on Web
search and data mining (pp. 533-542). ACM, 2012.
12. Bekkerman, R., McCallum, A.: Disambiguating web
appearances of people in a social network. In Proceedings of the
14th international conference on World Wide Web (pp. 463-470).
ACM, 2005.
13. Bethard, S., Yu, H., Thornton, A., Hatzivassiloglou, V.,
Jurafsky. D.: Automatic Extraction of Opinion Propositions and

their Holders. In: Proceedings of the AAAI Spring Symposium on
Exploring Attitude and Affect in Text, 2004.
14. Boiy, E. and Moens, M.: A Machine Learning Approach to
Sentiment Analysis in Multilingual Web Texts. Information
Retrieval, 12(5): 526-558, 2009.
15. Bollen, J., Mao, H., Pepe, A.: Modelling public mood and
emotion: Twitter sentiment and socio-economic phenomena. In
ICWSM, 2011.
16. Borgatti, S. P., Everett, M. G.: A graph-theoretic perspective on
centrality. Social networks 28, 466-484, 4, 2006.
17. Borgatti, S P.: "2-Mode concepts in social network analysis."
Encyclopedia of Complexity and System Science, 8279-8291, 2009.
18. Burke, R.: Hybrid recommender systems: Survey and
experiments. User Modelling and User-Adapted Interaction,
12(4):331–370, 2002.
19. Burt, R S.: Brokerage and closure: An introduction to social
capital. Oxford University Press, 2005.
20. Castellanos, M., Dayal, M., Hsu, M., Ghosh, R., Dekhil, M.: U
LCI: A Social Channel Analysis Platform for Live Customer
Intelligence. In: Proceedings of the 2011 international Conference
on Management of Data. 2011.
21. Chelmis, C., Prasanna. VK.: Social networking analysis: A state
of the art and the effect of semantics. Privacy, security, risk and
trust (passat), 2011 ieee third international conference on and
2011 ieee third international conference on social computing
(socialcom). IEEE, 2011.
22. Chen, Z. S., Kalashnikov, D. V. and Mehrotra, S. Exploiting
context analysis for combining multiple entity resolution systems.
In Proceedings of the 2009 ACM International Conference on
Management of Data (SIGMOD'09), 2009.
23. Chen, Y., Lee, K.: User-centred sentiment analysis on customer
product review. World Applied Sciences Journal 12 (special issue
on computer applications & knowledge management) 32 – 38,
2011. ACM, New York, NY USA, 2011.
24. Chou, W. Y. S., Hunt, Y. M., Beckjord, E. B., Moser, R. P.,
Hesse, B. W.: Social media use in the United States: implications
for health communication. Journal of medical Internet
research, 11(4), 2009.
25. Conover, M. D., Gonçalves, B., Ratkiewicz, J., Flammini, A.,
Menczer, F.: Predicting the political alignment of twitter users. In
Privacy, security, risk and trust (passat), 2011 ieee third
international conference on and 2011 ieee third international
conference on social computing (socialcom) (pp. 192-199). IEEE,
2011.
26. Cordeiro, M.: Twitter event detection: Combining wavelet
analysis and topic inference summarization. In Doctoral
Symposium on Informatics Engineering, DSIE, 2012.
27. Cortizo, J., Carrero, F., Gomez, J., Monsalve, B., Puertas, E.:
Introduction to Mining SM. In: Proceedings of the 1st
International
Workshop on Mining SM, 1 – 3, 2009.
28. Das, S., Chen, M.: “Yahoo! for Amazon: Extracting Market
Sentiment from Stock Message Boards,” In: Proceedings of the
Asia Pacific Finance Association Annual Conference (APFA),
2001.
29. Dave, KL., Pennock, D.: Mining the peanut gallery: Opinion
Extraction and Semantic Classiﬁcation of Product Reviews. In:
Proceedings of WWW 519-528, 2003.
30. Ding, X., B. Liu, Yu, P.: A Holistic Lexicon-based Approach to
Opinion Mining. In: Proceedings of the Conference on Web Search
and Web Data Mining (WSDM-2008), 2008.

31. Esuli, A., Sebastiani. F.: Determining the semantic orientation of
terms through gloss classification. In: Proceedings of ACM
International Conference on Information and Knowledge
Management (CIKM-2005), 2005.
32. Fortunato, S.: Community detection in graphs. Physics Reports,
486(3), 75-174, 2010.
33. Gamon, M., Aue, A., Corston-Oliver, S., Ringger, E.: Pulse:
Mining Customer Opinions from Free Text. Advances in
Intelligent Data Analysis VI, pages 121–132, 2005.
34. Ghosh, R., Lerman, K.: Parameterized centrality metric for
network analysis. Physical Review E, 83(6), 066118, 2011.
35. Girvan, M., Newman, M. E.: Community structure in social and
biological networks. Proceedings of the National Academy of
Sciences, 99(12), 7821-7826, 2002.
36. Godbole, N., Srinivasaiah, M., Steven, S.: Large Scale Sentiment
Analysis for News and Blogs. In: Proceedings of the International
Conference on Weblogs and SM (ICWSM), 2007.
37. Gomes, J. B., Adedoyin-Olowe, M., Gaber, M. M., Stahl, F.:
Rule type identification using TRCM for trend analysis in twitter.
In Research and Development in Intelligent Systems XXX (pp. 273-
278). Springer International Publishing, 2013.
38. Goldberg, A., and Zhu, X.: Seeing stars when there aren’t many
stars: Graph-based semi supervised learning for sentiment
categorization. In HLT-NAACL 2006 Workshop on Textgraphs:
Graph-based Algorithms for Natural Language Processing, 2004.
39. Hatzivassiloglou, V., McKeown, K.: Predicting the Semantic
Orientation of Adjectives. In: Proc. 8th Conf. on European chapter
of the Association for Computational Linguistics, Morristown, NJ,
USA, Association for Computational Linguistics 174 – 181, 1997.
40. Hoffman, T.: “Online Reputation Management is Hot — but is it
Ethical? Computerworld, 2008.
41. Hu, M., Liu, B.: Mining and Summarizing Customer Reviews.
KDD ’04. In: Proceedings of the tenth ACM SIGKDD
International Conference, 2004.
42. Jackson, M. O. Social and economic networks. Princeton
University Press, 2010.
43. Jindal, N., Liu. B. Mining Comparative Sentences and Relations.
In: Proceedings of National Conf. on Artificial Intelligence (AAAI-
2006), 2006.
44. Kagdi, H., Collard, M. L., Maletic, J. I.: A survey and taxonomy
of approaches for mining software repositories in the context of
software evolution. J. Softw. Maint. Evol.: Res. Pract, 19, 77-131,
2007.
45. Kaji, N., Kitsuregawa, M.: Automatic construction of polarity-
tagged corpus from HTML
documents. In: Processings of the COLING/ACL Main Conference
Poster Sessions, 2006.
46. Kaplan, A.M. and Haenlein, M.: Users of the world unite! The
challenges and opportunities of social media. Science direct, 53,
59-68, 2010.
47. Kaschesky, M., Sobkowicz, P., Bouchard, G.: Opinion Mining in
Social network: Modelling, Simulating, and Visualizing Political
Opinion Formation in the Web. In: The Proceedings of 12th
Annual International Conference on Digital Government Research,
2011.
48. Kaur, G.: Social network evaluation criteria and influence on
consumption behaviour of the youth segment. 2013.
49. Keshtkar, F., Inkpen, D.: Using sentiment orientation features for
mood classification in blogs. In Proceedings of the IEEE

International Conference on Natural Language Processing and
Knowledge Engineering (IEEE NLP-KE 2009), 2009.
50. Kim, P.: The Forrester Wave: Brand Monitoring, Q3 2006,”
Forrester Wave (white paper), 2006.
51. Kim, S., Hovy, E.: Determining the Sentiment of Opinions. In:
Proceedings of Intentional Conference on Computational
Linguistics (COLING-2004), 2004.
52. Kim, Y., Hsu, S-H., de Zuniga, H.G.: Influence of social
network use on discussion network heterogeneity and civic
engagement: The moderating role of personality traits. Journal of
Communication 63.3, 498-516, 2013.
53. Koppel, M. and Schler, J.: The importance of neutral examples
for learning sentiment computational intelligence, 22:100–109,
2006.
54. Korda, H., and Itani, Z.: Harnessing social network for health
promotion and behaviour change. Health promotion practice,
14(1), 15-23, 2013.
55. Ku, L.-W., Liang, Y.-T., Chen, H.-H.: Opinion extraction,
summarization and tracking in news and blog corpora. In Proc. of
the AAAI-CAAW'06, 2006.
56. Liu, F., Lee, H. J.: Use of social network information to enhance
collaborative filtering performance. Expert Systems with
Applications, 37, 4772-4778, 2010.
57. Liu, B.: Sentiment analysis and opinion Mining. AAAI-2011,
San Francisco, USA, 2011.
58. Mathioudakis, M. and Koudas, N.: Twittermonitor: trend
detection over the twitter stream. In Proceedings of the 2010 ACM
SIGMOD International Conference on Management of data (pp.
1155-1158). ACM, 2010.
59. McPherson, M., Smith-Lovin, L., Cook, J. M.: Birds of a feather:
Homophily in social networks. Annual review of sociology, 415-
444, 2001.
60. Morinaga, S., Yamanishi, K., Tateishi, K., Fukushima, T.:
Mining product reputations on the web. ACM SIGKDD 2002, 341-
349, 2002.
61. Murthy, D., Gross, A., Takata, A., Bond, S.: Evaluation and
Development of Data Mining Tools for Social Network Analysis.
In Mining Social Networks and Security Informatics (pp. 183-202).
Springer Netherlands, 2013.
62. Newman, M.: Networks: An introduction. Oxford University
Press, 2010.
63. Osborne, M., Petrovic, S., McCreadie, R., Macdonald, C., Ounis,
I.: Bieber no more: First story detection using twitter and
wikipedia.: In Proceedings of the Workshop on Time-aware
Information Access. TAIA Vol. 12, 2012.
64. Pang, B. and L. Lee, Vaithyanathan, S.: Thumbs up? Sentiment
classification using machine learning techniques. In: Proceedings
of Conference on Empirical methods in natural Language
Processing (EMNLP), Philadelphia, July 2002, 79 - 86. Association
for Computational Linguistics, 2002.
65. Pang, B. and Lee, L.: Seeing stars: Exploiting class relationships
for sentiment categorization with respect to rating scales,” In:
Proceedings of the Association for Computational Linguistics
(ACL), pp. 115–124, 2005.
66. Pang, B. and Lee, L.: Opinion mining and sentiment analysis;
Foundations and trends in information Retrieval; Vol. 2, Nos. 1–2,
1–135, 2008.
67. Pang, B. and Lee, L.: Using very simple statistics for review
search: An exploration,” In: Proceedings of the International

Conference on Computational Linguistics (COLING) (Poster
paper), 2008.
68. Papadopoulos, S., Kompatsiaris, Y., Vakali, A., Spyridonos, P.:
Community detection in social media. Data Mining and
Knowledge Discovery, 24(3), 515-554, 2012.
69. Pham, M. C., Cao, Y., Klamma, R., Jarke, M.: A clustering
approach for collaborative filtering recommendation using social
network analysis. J. UCS, 17(4), 583-604, 2011.
70. Phuvipadawat, S., and Murata, T.: Breaking news detection and
tracking in twitter. In Web Intelligence and Intelligent Agent
Technology (WI-IAT), 2010 IEEE/WIC/ACM International
Conference on (Vol. 3, pp. 120-123). IEEE, 2010.
71. Rahayu, D. A., Krishnaswamy, S., Alahakoon, O., Labbe, C.: RnR:
Extracting rationale from online reviews and ratings. In Data
Mining Workshops (ICDMW), 2010 IEEE International
Conference on (pp. 358-368). IEEE, 2010.
72. Rao, D. and Ravichandran, D.: Semi-supervised polarity lexicon
induction. In: Proceedings of the European Chapter of the
Association for Computational Linguistics (EACL), 2009.
73. Riloff, E., Wiebe, J., Wilson, T.: “Learning subjective nouns using
extraction pattern bootstrapping,” In: Proceedings of the
Conference on Natural Language Learning (CoNLL), 25–32, 2003.
74. Ruan, X. H., Hu, X., Zhang, X.: Research on Application Model of
Semantic Web-Based Social Network Analysis. In Proceedings of
the 9th International Symposium on Linear Drives for Industry
Applications, Volume 2 (pp. 455-460). Springer Berlin Heidelberg,
2014.
75. Santorini, B.: Part-of-speech tagging guidelines for the penn
treebank project (3rd revision, 2nd
printing). Technical Report,
Department of Computer and Information Science, University of
Pennsylvania , 1995.
76. Scott, J.: Social network analysis: developments, advances, and
prospects. Social network analysis and mining, 1(1), 21-26, 2011.
77. Sindhwani, V. and Melville, P.: Document-word co-regularization
for semi-supervised sentiment analysis. 8th IEEE International
Conference on Data Mining, 2008.
78. Symeonidis, P., Tiakas, E., Manolopoulos, Y.: Product
recommendation and rating prediction based on multi-modal social
networks. In Proceedings of the fifth ACM conference on
Recommender systems (pp. 61-68). ACM, 2011.
79. Tang, H., Tan, S., & Cheng, X.: A survey on sentiment detection of
reviews. Expert Systems with Applications, 36, 10760-10773, 2009.
80. Tepper, A.: How much data is created every minute?
[INFOGRAPHIC]. 2012, http://mashable.com/2012/06/22/data-
created-every-minute/. Retrieved on 16/102013 at 19.00.
81. Thompson, J B.: Media and modernity: A social theory of the
media. John Wiley & Sons, 2013.
82. Titov, I., McDonald, R.: A joint model of text and aspect ratings
for sentiment summarization. Proceedings of 46th Annual Meeting
of the Association for Computational Linguistics (ACL’08), 2008.
83. Tong, R.: An operational system for detecting and tracking
opinions in on-line discussion,” In: Proceedings of the Workshop
on Operational Text Classification (OTC), 2001.
84. Turney, P.: Mining the web for synonyms: PMI-IR Versus LSA on
TOEFL. In: Proceedings of the Twelfth European Conference on
Machine Learning 491-502. Berlin: Springer-Verlag, 2001.
85. Turney, P.: “Thumbs Up or Thumbs Down? Semantic orientation
applied to unsupervised Classification of Reviews,” In:
Proceedings of the Association for Computational Linguistics
(ACL), pp. 417–424, 2002.

86. Van de Camp, M., and Van den Bosch.: A Link to the past:
constructing historical social networks. In: Proceedings of the 2nd
Workshop on Computational Approaches to Subjectivity and
Sentiment Analysis, ACL-HLT 2011, 61–69, 24 June, 2011,
Portland, Oregon, USA 2011 Association for Computational
Linguistics , 2011.
87. Wang, H., Lu, Y., C. Zhai.: Latent aspect rating analysis on review
text data: A rating regression approach. In KDD '10, 783-792, New
York, NY, USA, 2010.
88. Weng, J. and Lee, B.-S.: Event detection in twitter. In ICWSM,
2011.
89. Wiebe, J. and Riloff, E.: “Creating subjective and objective
sentence classifiers from unannotated texts,” In: Proceedings of the
Conference on Computational Linguistics and Intelligent Text
Processing (CICLing), number 3406 in Lecture Notes in Computer
Science, 486–497, 2005.
90. Zhai, Z., Liu, B., Xu, H., & Jia, P.: Clustering product features for
opinion mining. In Proceedings of the fourth ACM international
conference on Web search and data mining (pp. 347-354). ACM,
2011.
91. Zhou, D., Councill, I., Zha, H., Giles, C.: Discovering temporal
communities from social network documents. Data Mining, 2007.
ICDM 2007. In: Proceedings of Seventh IEEE International
Conference on, 28 – 31 Oct, 2007, pages 745 – 750, 2007.
92. Zhou, L., Ding, L., & Finin, T.: How is the semantic web evolving?
A dynamic social network perspective. Computers in Human
Behaviour, 27(4), 1294-1302, 2011.

Survey of data mining techniques for social

More Related Content

What's hot (19)

Viewers also liked (8)

Similar to Survey of data mining techniques for social (20)

More from Firas Husseini (20)

Recently uploaded (20)

Survey of data mining techniques for social