The document outlines the work and achievements of the Sona Web team led by Dr. J. Akilandeswari, focusing on sentiment analysis, frequent pattern mining, and recommendation systems. Key projects include a scoring model for analyzing Twitter sentiments related to food prices and an efficient linear approach to eliminate redundant association rules in data mining. The team's publications and consultancy projects highlight their contributions to web services and development in various domains.

1. CENTRE FOR WEB SERVICES
AND DEVELOPMENT
(SONA WEB)

2. TEAM MEMBERS
Centre Head:
Dr. J.Akilandeswari, Professor and Head /IT
Team Members :
Dr. V. Mohanraj, Prof./IT
Dr. J. Jeba Emilyn, Asso.Prof./IT
Mr. J. Dhayanithi, AP/CSE
Ms. Anitha Elavarasi, AP/CSE
Mr. U.K. Balaji Saravanan, AP/IT
Ms. G. Jothi, Research Associate/IT
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3. OBJECTIVE
The centre addresses the challenges of research problems such as
 summarizing and analyzing the sentiment of the vast amounts of user-
generated content
 Analyzing the information in social networking websites
 Online recommendation system
 Ontology Ranking
 Dynamic Web service composition
 Dynamic data fusion of heterogeneous attributes
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4. I. Mining Frequent Patterns without Tree
Generation
 The frequent patterns are generated without any candidate set or tree
construction.
 This methodology encodes the database using numerical approach and
reduces the size of the encoded database to a great extent. .
 This algorithm leads to the reduction in both space and time complexity.
Journal Publication
 Shanthi K.V, Akilandeswari J and Jothi G, “Mining Frequent Patterns
Without Tree Generation”, International Journal of Data Mining,
Modelling and Management.(Paper Accepted)
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5. Mining Frequent Patterns without Tree
Generation
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6. II. Sentiment Analysis In Twitter Using Scoring
Model
 Twitter is one of the most fashionable microblogging that permits users to
express their views on sports, politics, modern technologies, movies and
spirituality and so on.
 It is hard to place the sentiment polarity of the tweets. In this paper new
scoring methodology to find the sentiment polarity of the twitter messages
is proposed.
 Emotions and shortened words are integrated to increase the significance of
the proposed scoring technique.
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7. II. Sentiment Analysis In Twitter Using Scoring
Model
The following work has been done
 We are now monitoring food-price related tweets from January 2015 around 5000
tweets and analyze the impact of food price crises.
 Statistical scoring model is used to classify the relevant tweets, depending on the
sentiment they express (i. e. “positive tweet” “negative tweet” and “neutral tweet”).
Sample of tweets was then used to train to classify the tweets in the correct category
and identify the sentiment of new tweets.
 Data mining techniques are used to extract the relevant keywords/ features related to
food price crises. From the twitter conversations, these keywords/features are analyzed
on how to correlate the impact of food price crises – developing an efficient new
algorithm for clustering the features
Journal Publication
 Akilandeswari J and Jothi G, “Sentiment Classification of Tweets using a Scoring
Model Incorporating Language & Non-language Features”, Applied for
International Journal of Information Retrieval Research.
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8. DATA ACQUISITION
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9. EXPERIMENTAL RESULTS
The overall score of the tweet is greater than zero, the tweets is classified as a
positive tweet, less than zero then classified as a negative tweet and closer to zero means
neutral tweet. Some sample tweets with the sentiment orientation is presented in the
Table 1.
Table 1. Sample tweets and Sentiment Polarity
Sl. No. Tweet Score Sentiment Polarity
1 Election this could get nasty -0.2000 Negative
2 I never reject this work 0.2200 Positive
3 The book is gud 0.1667 Positive
4
RT annual Spring Game is set for Saturday April at pm
ET in Commonwealth Stadium
0.0100 Neutral
5
With no respect for Indian cricket should be barred
from Villiers should go from IPL or be thrown out
-0.0111 Negative
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10. III. Elimination of Redundant Association Rules –
An Efficient Linear Approach
 Association rule mining plays an important role in data mining and knowledge
discovery.
 Traditional association rule mining algorithms generate lots of rules based on the
support and confidence values, many such rules thus generated are redundant.
 The eminence of the information is affected by the redundant association rules.
 The proposed algorithm removes redundant association rules to improve the quality of
the rules and decreases the size of the rule list.
 It also reduces memory consumption for further processing of association rules.
Publication
 J. Akilandeswari and G. Jothi, “Elimination of Redundant Association Rules – An
Efficient Linear Approach”, submitted the paper to the International Conference on
Computational Intelligence, Cyber Security and Computational Models
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11. CONSULTANCY
 Consultancy – SES – Smart Evaluation System – e-learning environment – IIT,
Bombay
 Website upgradation for JSW Salem Works
 Website upgradation for Co-Efficient Consulting Management, Netherlands
 Web Portal construction for Gujarati Samaj, Coimbatore
 Web site Construction for Special school for children (SMILE), Salem.
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12. PATENT AND PUBLICATIONS
Patent:
"A Method for Securing a Protocol" Application No: 3822/CHE/2014
Book Publications:
Released Second Edition of "Web Technology- A Developer's Perspective" during July
2014 by Prentice Hall of India
No. of International Journal Publications :
No. of National Journal Publications :
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13. RECOMMENDATION SYSTEM
Objective
- Capture the user intent and recommend the web pages the
contains user expected information.
- An important challenge of such system must include a need of
being self-adaptive because the needs of online user may
change dynamically and also design an accurate classifier for
improving the accuracy of recommendation system.
Publication: “Ontology Driven Bee’s Foraging Approach based
Self Adaptive Online Recommendation System”, Journal of
Systems and Software, Elsevier, Vol. 85, No.11, pp. 2439-
2450, 2012. Impact Factor: 1.352 and 5 – Year Impact
Factor: 1.485
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14. Current Progress in Recommendation System
• Personalized Recommendation using CF and CBF methods
in conjunction with Social Networks Data and Geo-tag. Handling cold start
problem in CF based Recommendation system.
- Received the consent for INDIA-TAIWAN project for the year
• 2017 from
• Prasan Kumar Sahoo, PhD(CSE), PhD(Math), MTech(IIT, Kgp)
Director, International Academic Affairs Center
Dept. of Computer Science and Information Engineering,
Chang Gung University 259, Wen-Hwa 1st Road, Guie-Shan, 3302,
TAIWAN
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15. DATA MINING IN BIOLOGICAL DATA
Objective
Design a rough set based Bi clustering algorithm for efficiently finding useful
pattern in gene expression.
Publication:
• “Community Detection And Identifying Leaders And Followers In Online
Social Networks”, International Journal Of Scientific & Engineering
Research, V0l-6, Apr-2015
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16. CONTACT
Contact Details:
Dr. J. Akilandeswari,
Professor and Head,
Department of Information Technology,
Sona College of Technology,
Salem.
Ph : 0427 4099755
Mobile: 9894777003
Email: akilandeswari@sonatech.ac.in
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