Applying Clustering Techniques for Efficient Text Mining in Twitter Data

Integrated Intelligent Research (IIR) International Journal of Web Technology
Volume: 04 Issue: 02 December 2015 Page No.77-80
ISSN: 2278-2389
77
Applying Clustering Techniques for Efficient Text
Mining in Twitter Data
K.T.Mathuna , I.Elizabeth Shanthi, K.Nandhini
Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore
Email: mathuna.thangaraj@gmail.com, shanthianto@gmail.com, nandhinik2@gmail.com,
Abstract- Knowledge is the ultimate output of decisions on a
dataset. The revolution of the Internet has made the global
distance closer with the touch on the hand held electronic
devices. Usage of social media sites have increased in the past
decades. One of the most popular social media micro blog is
Twitter. Twitter has millions of users in the world. In this
paper the analysis of Twitter data is performed through the text
contained in hash tags. After Preprocessing clustering
algorithms are applied on text data. The different clusters
formed are compared through various parameters.
Visualization techniques are used to portray the results from
which inferences like time series and topic flow can be easily
made. The observed results show that the hierarchical
clustering algorithm performs better than other algorithms.
Keywords- Text mining, Visualization, Twitter, Clustering,
Time series analysis, Social media.
I. INTRODUCTION
The communication of people has greatly changed in recent
years, one of the most important being social media
networking [1]. Nowadays, social media is not only used for
personal networking but also for commercial purposes.
Happenings in the real world is shared and communicated via
Internet. Internet forms a bridge between the users irrespective
of the global distance. The most popular social sites includes
Face book, Twitter and You tube [2]. The social media has
opened up many research opportunities because of the
increased amount of information.
A. Background
This paper deals with the analysis of the topic and event
detection on the social micro blogs. Particularly, Twitter which
is widely used and fast growing in real world blog. More than
500 million users are assessing twitter and above 302 are active
users which generate about 340 million messages everyday
[1][3]. People upload their opinion and real world happenings
in this public site. Current topics and trends are the main
features of twitter. Twitter provides “hash tag (#)”, which is
used for providing topics for tweets. If this hash tag is used by
many people then the topic becomes the current trending topic
[4]. Hence gathering, mining and analyzing tweets has its own
importance in all areas. This paper aims in analyzing the
efficient text mining algorithms via clustering techniques.
Text Mining- Text mining, also known as text data mining or
knowledge discovery process from the textual databases is
generally the process of extracting interesting and non-trivial
patterns or knowledge from unstructured text documents. Text
Mining is similar to data mining, for storing information in
structured manner and on the contrary text mining uses texts
that are unstructured or semi-structured. The data is collected
through World Wide Web, Linked In, government portals, face
book, twitter, blog, news articles, digital libraries, electronic
mail and so on. Approximately 80% of the organizational
information is stored in unstructured form [5].
B. Applications and Issues in Text Mining
Text mining has its roots in almost all the areas. Text is used
everywhere and has to be mined for information retrieval and
knowledge gathering. It is applied in fields like publishing,
media, telecommunications, research, banks, insurance,
finance, government administration, legal documents, health
care, business intelligence, national security, etc. All these
fields have been improved and still waiting for betterment. The
major challenge arises from the natural language processing of
large information to extract required knowledge. Complexity
of extraction and computational cost initiates the need for
further improvement. Unstructured and multiple forms of text
documents makes the retrieval process challenging. Research
work is still in need on issues like text mining that uses
different intermediate forms, integration of domain knowledge,
analysis of social media network, etc [6].
C. Integration of Text Mining and Visualization
Visualization aids in better understanding of the extracted
content from the raw database. It further gives a clear picture
of the information that has to be delivered. Text mining
integrated with visualization provides a better and fast
understanding of interpreted results. Text visualization has two
forms, Topic based and Feature based. In Topic based method,
topics and events are visualized through visualization
techniques. Some of the techniques include Tag clouds which
depict the keywords or named entities. They use features like
colour, size and layout based on usability and importance.
Information landscape provides a geographical view of large
set of documents for analysis. Text Flow method combines
topic mining and interactive visualization techniques to
visually analyze the evolution of topics in due course. In
Feature based method, Word clouds are commonly generated
to provide an intuitive visual summary of documents by
displaying the keywords in a compact layout. Facet Atlas
method integrates node-link diagram with density map to
visually analyze the multifaceted relations of the document
[7].The organization of the paper is as follows. Section II deals
with the recent research. Section III tells about the
methodology and algorithms used. Section IV explains the
experimental results and discussions and finally in Section V
conclusion is given.

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II. LITERATURE REVIEW
Recent research work on Twitter data has been in various
topics. Density based clustering, naïve based and other
techniques used in research along with their observations are
listed in Table 1.
Table 1: Review of the Related Work
Authors Technique(s) used
Observations
Year
Chung-
Hong Lee
[8]
Density-based
clustering method is
used to mine micro
blogging text
streams.
Temporal and
spatial features of
real world events
are analyzed and
estimated.
2012
Rischan
Mafrur et
al.[4]
Naïve Bayes
Classification
technique was
applied and word
cloud visualization
method was used.
Analysis about
the election
campaign
through twitter
shows the
positive and
negative
comments from
people.
2014
Farhan
Hassan
Khan et
al.[9]
Proposed hybrid
model for
classification of
tweets. Enhanced
Emoticon Classifier
(EEC), Improved
Polarity Classifier
(IPC) and
SentiWordNet
Classifier (SWNC)
where used.
The hybrid
model showed
better accuracy. It
decreases the
neutral opinion
by correctly
classifying into
positive or
negative
sentiment.
2014
Isti
Surjandari
et al.[10]
Support vector
machine (SVM),
naïve bayes and
decision tree
algorithms are used
to classify. Chi
squared test and
Marascuilo
procedure were
performed for
framing association
rules.
Support vector
machine resulted
with higher
accuracy in
analyzing public
opinion.
2015
Janez
Kranjc et
al.[11]
ClowdFlows, cloud-
based scientific
workflow platform
with widget
memory and halting
mechanism was
created. Word cloud
and stream based
visualization is
given.
A web service is
built to apply the
models on
unlabeled tweets.
2015
Shakira
Banu
Kaleel et
al.[12]
Locality sensitive
hashing (LSH) and
K-means algorithm
is used to form
Locality sensitive
hashing (LSH)
improved
accuracy and
2015
Authors Technique(s) used
Observations
Year
detecting and
trending events
form twitter
clusters. Word cloud
and Google maps
are used.
runtime.
Form the recent study, Support vector machine, naïve base
classifier, density based clustering and k-means clustering
algorithms were applied and the observations show that works
are done on sentiment analysis to improve the accuracy. Works
based on current issues and real world happenings still have a
wider scope in research.
III. METHODOLOGY
A. Overview of Methodology
Tweets are gathered from twitter for text mining process. The
user generated twitter data contains slang, noise and
grammatical mistakes. These have to be cleaned for improving
the quality of the tweet features [11]. Preprocessing in text data
involves Stop word removal, Stemming, Converting upper
cases to lower, removing punctuations and numbers. This
makes the text more content specific. Stop word removal aims
in removing stop words which has no meaning when it is
single. Articles, prepositions, pro-nouns and conjunctions are
the most common stop words which includes words like is, the,
an, but, for etc. These words have to be removed to make text
processing fewer complexes to facilitate the reduction in the
number of words for retrieval [13, 14]. Stemming removes
affixes in a word leaving the root word. For example the words
study, studied, studying, studies gives the root word “study”.
This method reduces the indexing structure size as the numbers
of distinct index terms are reduced [14, 15]. Finally converting
upper case lower, removing punctuations and numbers reduces
the retrieval complexity. These methods of preprocessing make
the text corpus less complex for text mining.
Figure1. Overview of Methodology
The preprocessed text data is given as input to the clustering
algorithms. Clustering is an unsupervised technique; it aims in
grouping similar words in the same cluster and dissimilar

ISSN: 2278-2389
79
words in different clusters [16]. The K-means, Entropy
weighted K-means and Hierarchical algorithms are some of the
most commonly used algorithms for text mining process. K-
means algorithm is the basic and popular algorithms in text
mining and used for large set of data. Entropy weighted K
means (EWKM) algorithm is mainly used in Rattle data
mining. Ewkm algorithm includes weighting scheme for
clustering data and used for high dimensional data [17, 18].
Hierarchical Clustering algorithm forms clusters based on the
dissimilarity between the observations. This algorithm decides
when a cluster has to be splitted and when clusters have to be
joined [16].These algorithms are applied in Rattle environment
in R language. Visualization is the communication of data
through the use of interactive graphical user interfaces. In text
mining visualization methods can improve and simplify the
discovery or extraction of relevant patterns of information [19].
IV. EXPERIMENTAL RESULTS AND DISCUSSIONS
The twitter dataset is taken from the Sentiment 140 website for
academics [20]. R language and environment for statistical
computing and graphics is used to provide a wide variety of
statistical and graphical techniques, and is highly extensible.
Tweets are preprocessed through text preprocessing methods
and then applied on the clustering algorithms. K-means,
Entropy Weighted K-means and Hierarchical clustering
algorithms form clusters of similar data. The words that are
similar in tweets are grouped together in single cluster and
dissimilar words are grouped in different clusters. These
clusters formed are evaluated based on the internal cluster
validation parameters that are commonly used. Parameters like
Average Between, Average Silhouette Width, Pearson gamma,
Dunn and Dunn2 are calculated for all the three algorithms and
compared to find the best applying algorithm. The experiment
was performed on cluster size increasing from 2 to 20, on
preprocessed twitter data but the optimal and standard values
for the data set have been obtained for cluster size 6 to 10. The
optimal cluster size was obtained based on experiments
conducted. Table 2 lists observed values of these parameters
for the clustering algorithms used.It is clear from Table 2 that
Hierarchical algorithm performs better than the other two
clustering algorithms for clusters of size ranging from 6 to 10.
In this research work visualization methods are applied to the
results interpreted from Hierarchical clustering algorithm.
Word cloud is used to display the frequency of words that
occur in twitter data. Time series analysis is one important
issue in the current scenario. The day to day happenings are
recorded and interpreted for knowledge discovery, information
retrieval and decision making. Area plot is created using Plotly
package in R language. This shows the time line of the topic
that is tweeted. The frequency of a topic that is currently
tweeted decides on the current trending news which is given
through 2D Histograms. Figure 2(a) shows the word cloud of
tweets, Figure 2(b) shows the time series visualization of
twitter data and Figure 2(c) shows the topic frequency of
twitter data. The interpreted results are visualized through
visualization methods.
Table 2. Comparison of Clustering Algorithms
(a)
(b)

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80
(c)
Figure 2. (a) Word cloud Visualization, (b) Time series
analysis, (c) Topic trends of Twitter data.
V. CONCLUSIONS AND FUTURE SCOPE
The results of this study show the clustering of twitter data.
Twitter is fast growing and widely used social networking site
on the World Wide Web. Mining of Twitter data has gained
importance in the past decades. Integrating both text mining
and visualization provides better knowledge on information
discovery and decision making. This work can be further
applied on medical text mining, government portals, etc so that
they could be used for national security and rehabilitation due
to natural disasters.
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