Monitoring opinion on esop through social media and clustering its polarity

Abstract
Facebook and Twitter started off as friendship
and networking tool, but they have evolved into
potent weapons of social mobilization. This tools
not only monitors users who are actively
engaged in providing sentiment, or the opinion
(attitude) but also they add their unique insights
to product penetration and reflect the changing
moods of the public. In this paper we were
about to collect feedback on ESOP at a very
basic level through different social monitoring
tools and observe their polarity. The proposed
approach combines K-means clustering,
Expectation Maximisation clustering algorithm
and VAR K-Means that can be used to group
the monitoring tools within a particular time
span. A data mining tool Tanagra1.4 can be
used for implementation of their results.
Keywords:
Social media, Sentiment Analysis, Opinion
Mining, Social Monitoring Tools, Tanagra1.4
1. SOCIAL MEDIA
Social media are popularly known
as democracy’s pipeline, an amplifier of
unfiltered emotion, an organism with a million
tongues and twice as many eyes, a virtual
megaphone with a global reach. This networking
tool is now a weapon of public mobilization. The
unbridling of the power of the social media was
undoubtedly a top, if not number one trend of
2012 in India. In many cases, it set the agenda of
public discourse. It is a movement without
leaders, without any organized structure, and
without any pre-determined plan. Definitely it is
fundamentally making new shape. Recent surveys
on media by research firm Social Bakers and
Semiocast a Paris reports that:-
1. Facebook has 65 million active users in India –
top five world-wide in terms of users.
2. Half of Indian facebook users are below the
age of 25years.
3. 75% of web users in India below age of
35years.
4. One in every four online minutes spent on
social networking sites.
5. Over 200mn Twitter users globally, Make half
a billion tweets 6th
in terms of total twitter
accounts.
6.42% smartphone users in India use device to
access news.
7. Nearly 72% netizens lives in urban areas.
8. Nearly 52% internet users connect to web via a
mobile phone.
9. Among urban internet users 67% connect to
web for social networking, 87% for
communication.
10. About 1.5Lakh new internet users added
every month in India.
2. SENTIMENT ANALYSIS
In General, Opinion mining or Sentiment
analysis is an important sub discipline within data
mining and Natural Language Processing (NLP),
that deals with building a system that explores
the user’s opinions made in blog spot, comments,
reviews, discussion, news, feedback or tweets,
about the product, policy, person or a topic.
Sentiment analysis, opinion mining, opinion
extraction, sentiment mining, subjectivity
analysis, affect analysis, emotion analysis, review
mining, etc. are now under the umbrella of
sentiment analysis or opinion mining. All these
platforms provide a huge amount of valuable
information that we are interested to analyse. To
be specific, opinion mining can be defined as a
sub-discipline of computational linguistics that
focuses on extracting people’s opinion from the
web. It analyse from a given piece text about: -
Which part is opinion expressing; Who wrote the
opinion; What is being commented. Sentiment
analysis on the other hand is about determining
the subjectivity, polarity (positive, negative or
neutral) and polarity strength (weakly positive,
mildly positive, strongly positive, etc.,). In other
Monitoring Opinion on ESOP through Social Media
and Clustering its Polarity
Page 1

words it will look into piece of text for: - What is
the opinion of the writer.
2.1. SENTIMENT ANALYSIS RESEARCH
The urban educated reader, may first
check out the blogs, read product reviews and
decide on brands they choose be it new LED
TVs, home décor, DVDs, insurance plan and
policies etc,. In general, sentiment analysis has
been investigated mainly at three levels:
1. Document level: The task at this level is to
classify whether a whole opinion document
expresses a positive or negative sentiment. For
example, given a product review, the system
determines whether the review expresses an
overall positive or negative opinion about the
product. This task is commonly known as
document-level sentiment classification. This
level of analysis assumes that each document
expresses opinions on a single entity (e.g., a
single).
2. Sentence level: The task at this level goes to
the sentences and determines whether each
sentence expressed a positive, negative, or neutral
opinion. Neutral usually means no opinion. This
level of analysis is closely related to subjectivity
classification, which distinguishes sentences
(called objective sentences) that express factual
information from sentences (called subjective
sentences) that express subjective views and
opinions. However, we should note that
subjectivity is not equivalent to sentiment as
many objective sentences can imply opinions,
e.g., “We bought the car last month and the
windshield wiper has fallen off.”
3. Entity and Aspect level: Both the document
level and the sentence level analyses do not
discover what exactly people liked and did not
like. Aspect level performs finer-grained analysis.
Instead of looking at language constructs
(documents, paragraphs, sentences, clauses or
phrases), aspect level directly looks at the opinion
itself. It is based on the idea that an opinion
consists of a sentiment (positive or negative) and
a target (of opinion). An opinion without its
target being identified is of limited use. Realizing
the importance of opinion targets also helps us
understand the sentiment analysis problem better.
For example, the sentence “The iPhone’s call
quality is good, but its battery life is short”
evaluates two aspects, call quality and battery
life, of iPhone (entity). The sentiment on
iPhone’s call quality is positive, but the sentiment
on its battery life is negative. The call quality and
battery life of iPhone are the opinion targets.
Based on this level of analysis, a structured
summary of opinions about entities and their
aspects can be produced.
3. SENTIMENT MONITORING TOOLS
Social media has created a new world of
venting and consumer voice. This changing
online environment has allowed customers to
comment about brands and personal experiences.
And that is why it is necessary to perform social
media monitoring. One helpful aspect in
monitoring is sentiment, or the attitude and tone
of a user’s comment, review or mention with
respect to the brand. There are hundreds of
sentiment analysis programs available—but most
come with a cost.
4. PROPOSED WORK:
Here, we have taken in our proposed work
the document level sentiment towards ESOP
through the best four sentiment monitoring tools.
1.Social Mention—track and measure what
people are saying about you, your company, a
new product, policy or any topic across the
Web’s social media landscape (100+ social media
platforms)
Fig 1: Social mention feedback on ESOP
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2. Trackur —Online reputation and social media
monitoring tool to track trends, understand
influence, receive alerts and tag sentiment.
Fig 2: Trackur feedback on ESOP
3. Twendz—A Twitter-mining Web application
that highlights conversation themes and sentiment
of the tweets, as well as pinpointing top
influencers minute by minute.
Fig 3:Twendz feedback on ESOP
4.Twitrratr—Simply analyse terms based on a
pre-defined glossarly, and give highly simplified
and unreliable results.
Fig 4: Twitratr feedback on ESOP
5. CLUSTERING USING TANAGRA1.4
Clustering is one of the important
techniques in data mining categorizes unlabeled
objects into several clusters such that the objects
belonging to the same cluster are more similar
than those belonging to different clusters. A
cluster is an ordered list of objects, which have
some common characteristics. The objects
belong to an interval [a,b] or [0,1].The distance
between two clusters involve some or all
elements of the two clusters.Tanagra1.4 is free data
mining software for academic and research purposes.
It proposes several data mining methods from
exploratory data analysis, statistical learning, machine
learning and databases area. The main purpose of
Tanagra project is to give researchers an easy-to-use
data mining software. The second purpose of
TANAGRA is to propose to researchers an
architecture allowing them to easily add their own
data mining methods, to compare their performances.
TANAGRA acts more as an experimental platform.
The third and last purpose, in direction of novice
developers, consists in diffusing a possible
methodology for building this kind of software. In
this way, Tanagra can be considered as a pedagogical
tool for learning programming techniques.
6. DATASAMPLE
The dataset used in our experimental
research is acquired from various social
monitoring tools then it is imported to
Tanagra1.4. First step is to use feature selection
components to define status and parameter. Next,
step is to click on the clustering component and
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choose K-means method. The other two
clustering methods must follow same procedure
in defining its status.
Table 1: Polarity strength towards ESOP
Tool
used
Time
Taken
No of
Positive
polarity
No of
negative
polarity
No of
Neutral
polarity
Total
Number
Of
tweets
Social
mention 9 13 0 284 297
Twendz
6 21 3 76 100
Trackur
5 7 6 1 13
Twittrat 3 17 1 153 171
Fig 5: In Tanagra1.4 view dataset window
7. IMPLEMENTATION OF ALGORITHM
7.1. K-Means is a well-known partition method.
Objects are classified as belonging to one of k groups.
Cluster membership is determined by calculating the
centroid for each group and assigning each object to
the group with the closest centroid. This approach
minimizes the overall within-cluster dispersion by
iterative reallocation of cluster members.
Description: Clustering with K-Means method (Forge
or McQueen) continuous input attribute.
Precondition: One or more continuous attributes must
be available in the dataset.
Target attribute(s): None
Input attribute(s): One or more continuous
attributes.
Post condition: A new discrete attribute is added to
the dataset. Each value of the attribute corresponds to
a cluster.
Fig 6:In K-Means R-Square calculation for
each trial
Fig 7: K-Means – Cluster centroids
7.2. The Expectation Maximization(EM) is a well-
established clustering algorithm in the statistics
community. EM is a distance-based algorithm that
assumes the dataset can be modeled as a linear
combination of multivariate noraml distributions and
the algorithm finds the distribution parameters that
maximize a model quality measure called log
likelihood.
Description:Clustering with Expectation-Maximization
clustering algorithm. Gaussian mixture. Continuous
inputs.
Precondition: One or more continuous attributes must
Input attribute(s):One of more continuous attributes.
Post condition:A new discrete attribute is added to the
dataset. Each value of this attribute corresoponds to a
cluster.
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Fig 8: In EM-clustering, cluster quality criteria –
log likelihood is calculated
Fig 9: In EM-clustering Cluster centroid
7.3.VAR K-Means:
Description:Clustering variables using K-Means
approach on latent variable.
Precondition: Two or more continuous attributes must
Input attribute(s):Two or more continuous attributes.
Post condition:A set of continuous attributes which
represent clusters are available.
Fig 10 : Cluster members and R-Square value
Fig 11: VAR K-Means, Cluster correlat
7.CONCLUSION
After analyzing the results using three
different clustering algorithm that runs under
Tanagra1.4 tool, the following tables and charts are
generated which indicates cluster 2 has indicates most
negative value when compared to cluster 1 and cluster
2 irrespective of the algorithm choosen.
Table 2: No of positive polarity:
Algorithm cluster1 cluster2 cluster 3
K-means 19.000000 19.000000 1.0000
EM 7.000000 19.000000 0.2075
VAR k-
means 13.000000
-
99999.000000 -0.4852
Table 3: No of negative polarity:
K-means 2.000000 2.000000 -0.4852
EM 6.000000 3.000000 -0.9373
VAR k-
means 0.000000
-
99999.000000 1.0000
Table 4: No of neutral polarity:
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K-means 114.500000 114.500000 0.2075
EM 0.000000 142.000000 1.0000
VAR k-
means 284.000000
-
99999.000000 -0.9373
Fig 12:Chart about positive polarity
Fig 13: Chart about negative polarity
Fig 14: Chart indicating neutral polarity
REFERENCES
[1] Zhongwu Zhai, Bing Liu, Hua Xu and Peifa
Jia. "Clustering Product Features for Opinion
Mining." Proceedings of Fourth ACM
International Conference on Web Search and
Data Mining (WSDM-2011), Feb. 9-12, 2011,
Hong Kong, China.
[2] Bo Pang and Lillian Lee,”Opinion Mining and
Sentiment Analysis”, Foundations and Trends R_ in
Information Retrieval,Vol. 2, Nos. 1–2 (2008) 1–135
[3] K.Nirmala Devi and V.Murali Bhaskaran,
“Sentiment Analysis for Online Forums Hotspot
Detection”, Proceedings in ICTACT Journal on Soft
Computing, Vol. 02,No.2, Jan 2012.
[4] Osama Abu Abbas,”Comparison s Between Data
Clustering Algorithms”,in International Arab Journal
of Information Technology, Vol. 5, No.3, July 2008.
[5] Georgious Paltoglou, Mike Thelwall,” Twitter,
MySpace, Digg: Unsupervised sentiment analysis in
social media”,ACM-TIST-V3N4-TIST-2010-11-0317
[6] Albert Bifet and Eibe Frank,University of
Waikato, Hamilton, New Zealand, ”Sentiment
Knowledge Discovery in Twitter Streaming Data”
[7] Minqing Hu and Bing Liu. "Mining and
summarizing customer reviews." Proceedings of
the ACM SIGKDD International Conference on
Knowledge Discovery & Data Mining (KDD-
2004, full paper), Seattle, Washington, USA, Aug
22-25, 2004.
[8] www.socialmention.com, www.twendz.com,
www.trackur.in, www.twitratr.com,
www.eBizMBA.com
Nithya Ramachandran is working as
Assistant Professor in Computer
science Department at R.V.S College
of Arts and Science, Sulur,
Coimbatore, Tamil Nadu, India and pursuing
Ph.D in part time in the area of Data mining.Her
research work focusses on datamining and its
supporting open source tools.
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