Data enter total cost of ownerships (TCO) tools and spreadsheets can be used to estimate the capital and operational costs required for running datacenters. These tools are helpful for business owners to improve and evaluate the costs and the underlying efficiency of such facilities or evaluate the costs of alternatives, such as off-site computing. Well understanding of the cost drivers of TCO models can provide more opportunities to business owners to control costs .In addition, they also introduce an analytical structure in which anecdotal information can be cross-checked for consistency with other well-known parameters driving data center costs. This work focuses on comparing between number of proposed tools and spreadsheets which are publicly available to calculate datacenter total cost of ownership (TCO) ,The comparison is based on many aspects such as what are the parameters included and not included in such tools and whether the tools are documented or not. Such an approach presents a solid ground for designing more and better tools and spreadsheets in the future.

1. TTOOPP 55 MMOOSSTT VVIIEEWWEEDD
AARRTTIICCLLEESS FFRROOMM AACCAADDEEMMIIAA
International Journal of Computer Science,
Engineering and Applications (IJCSEA)
ISSN: 2230 - 9616 [Online]; 2231 - 0088 [Print]
http://airccse.org/journal/ijcsea/index.html

2. DATACENTRE TOTAL COST OF OWNERSHIP (TCO)
MODELS: A SURVEY
Doaa Bliedy, Sherif Mazen and Ehab Ezzat
Department of Information System, Cairo University, Cairo
ABSTRACT
Data enter total cost of ownerships (TCO) tools and spreadsheets can be used to estimate the capital
and operational costs required for running datacenters. These tools are helpful for business owners to
improve and evaluate the costs and the underlying efficiency of such facilities or evaluate the costs of
alternatives, such as off-site computing. Well understanding of the cost drivers of TCO models can
provide more opportunities to business owners to control costs .In addition, they also introduce an
analytical structure in which anecdotal information can be cross-checked for consistency with other
well-known parameters driving data center costs. This work focuses on comparing between number of
proposed tools and spreadsheets which are publicly available to calculate datacenter total cost of
ownership (TCO) ,The comparison is based on many aspects such as what are the parameters included
and not included in such tools and whether the tools are documented or not. Such an approach
presents a solid ground for designing more and better tools and spreadsheets in the future.
KEYWORDS
Datacenters, TCO model, TCO parameters, IT software license cost, network, server.
For More Details: http://aircconline.com/ijcsea/V8N4/8418ijcsea04.pdf
Volume Link: http://airccse.org/journal/ijcsea/current2018.html

3. REFERENCES
[1] J. G. Koomey, "Worldwide electricity used in data centers," Environmental research letters,
vol. 3, p. 034008, 2008.
[2] W. P. Turner IV and K. G. Brill, "Cost model: Dollars per kW plus dollars per square foot of
computer floor," Uptime Institute, 2008.
[3] T. ASHRAE, "Datacom equipment power trends and cooling applications," ed: American
Society of Heating, Refrigerating and Air‐Conditioning Engineers, Inc. Atlanta, 2005.
[4] N. CRAFT, "Explain: Tier 1 / Tier 2 / Tier 3 / Tier 4 Data Center”, nixCraft Website, 29 January
2011," ed.
[5] C. Belady, "Projecting annual new datacenter construction market size," Microsoft, Global
Foundation Services Report, 2011.
[6] N. Rasmussen, "Determining total cost of ownership for data center and network room
infrastructure," Relatóriotécnico, Schneider Electric, Paris, vol. 8, 2011.
[7] C. D. Patel and A. J. Shah, "Cost model for planning, development and operation of a data
center," ed, 2005.
[8] J. Karidis, J. E. Moreira, and J. Moreno, "True value: assessing and optimizing the cost of
computing at the data center level," in Proceedings of the 6th ACM conference on Computing
frontiers, 2009, pp. 185-192.
[9] J. D. Moore, J. S. Chase, P. Ranganathan, and R. K. Sharma, "Making Scheduling" Cool":
Temperature-Aware Workload Placement in Data Centers," in USENIX annual technical
conference, General Track, 2005, pp. 61-75.
[10] J. Koomey, K. Brill, P. Turner, J. Stanley, and B. Taylor, "A simple model for determining
true total cost of ownership for data centers," Uptime Institute White Paper, Version, vol. 2,
p. 2007, 2007.
[11] K. V. Vishwanath, A. Greenberg, and D. A. Reed, "Modular data centers: how to design
them?," in Proceedings of the 1st ACM workshop on Large-Scale system and application
performance, 2009, pp. 3-10.

4. [12] Datacenter capital cost calculator
https://www.schneiderelectric.com/en/work/solutions/system/s1/data-center-and-network-
systems/trade-off-tools/datacenter-capital-cost-calculator/tool.html.
[13] Virtualization Energy Cost Calculator
https://www.schneiderelectric.com/en/work/solutions/system/s1/data-center-and-network-
systems/trade-off-tools/datacenter- virtualization-energy-savings-calculator/tool.html
[14] J. Hamilton, "Overall data center costs," Perspectives at
http://perspectives.mvdirona.com/2010/09/18/ overalldatacentercosts.aspx. mvdirona. com,
2010.
[15] Overall data center costs
http://mvdirona.com/jrh/TalksAndPapers/PerspectivesDataCenterCostAndPower.xls
[16] A simple model for determining true total cost of ownership for data centers
https://virtualizationandstorage.files.wordpress.com/2014/05/truetco_model_2-1.xls
[17] W.Torell, “Data Center Capital Cost Calculator – A Tool to Help Align Your Data Center
Business Requirements with Your Project Budget”
https://blog.schneiderelectric.com/datacenter/2012/06/28/data-center-capital-cost-calculator-a-
tool-to-help-align-your-datacenter- business-requirements-with-your-project-budget/
[18] Oanda https://www.oanda.com/currency/converter/
[19] P. Niles and P. Donovan, "Virtualization and Cloud Computing: Optimized Power, Cooling,
and Management Maximizes Benefits. White paper 118. Revision 5," Schneider Electric,
Tech. Rep2015.

5. A MULTI-CHANNEL SYSTEM ARCHITECTURE FOR
BANKING
Chris Pavlovski
IBM, Sydney, NSW, Australia
ABSTRACT
Financial institutions have increased dependence upon the technology solutions that enable their
financial products and services. The proliferation of Internet technologies, mobile devices, and
competition from international commerce have placed increased pressure up banking and financial
institutions to ensure that competitive leadership is maintained. A key challenge facing banking and
finance is how to adopt the new Information and Communications Technologies (ICT) within the
organization in a timely manner whilst not disrupting the embedded solutions that provide core
banking capabilities to operate the business. In this paper we propose a multi-channel architecture for
financial institutions such as banking. The architecture is based upon our industry experience in
developing multi-channel solutions in similar industries and is refined further here based upon our
experiences in banking. The proposed architecture may be used to facilitate decisions on how best to
deploy new and emerging multi-channel technologies within the banking environment, providing a
means for assessing how to ensure effective use of existing investments in systems and technologies.
The solution may be used as a blueprint for banking institutions in developing their multi-channel
strategies; addressing the incumbent, emerging, and future channels of banking distribution.
KEYWORDS
Banking, Multi-channel Architecture, Pervasive Computing, Emerging Technologies.
For More Details: http://airccse.org/journal/ijcsea/papers/3513ijcsea01.pdf
Volume Link: http://airccse.org/journal/ijcsea/current2013.html

6. REFERENCES
[1] C.J. Pavlovski, “Service Delivery Platforms in Practice”, IEEE Communications Magazine, vol.
45, no. 3, March 2007, pp. 114-121.
[2] T. Kamogawa and H. Okada, “Enterprise Architecture and Information Systems - In
Japanese Banking Industry”, International Symposium on Applications and the Internet. IEEE,
2008, pp. 433-436.
[3] J. Sun and Y. Chen, “Building a Common Enterprise Technical Architecture for an Universal
Bank”, International Conference on Management and Service Science (MASS), 2010, pp. 1-4.
[4] R. Winter and R. Fischer, “Essential Layers, Artifacts, and Dependencies of Enterprise
Architecture”, Journal of Enterprise Architecture, Vol. 3, Issue 2, May 2007.
[5] K. Pousttchi and M. Schurig, “Assessment of Today’s Mobile Banking Applications from the
View of Customer Requirements”, Proceedings of the 37th Hawaii International Conference
on System Sciences - 2004, pp. 1-10.
[6] M.K. Harma and R. Dubey, “Prospects of technological advancements in banking sector using
Mobile Banking and position of India”, International Association of Computer Science and
Information Technology, 2009, pp. 291-295
[7] P. Chandrahas, D. Kumar, et. al., “Some Design Considerations for a Mobile Payment
Architecture”, National Conference on Communications (NCC), 2011, pp. 1-5.
[8] S.K. Bhosale, “Architecture of a Single Sign on (SSO) for Internet Banking”, IET International
Conference on Wireless, Mobile and Multimedia Networks, 2008. IET pp. 103-105.
[9] C.Narendiran, S.A. Rabara, and N.Rajendran, “Performance Evaluation on End-to-End Security
Architecture for Mobile Banking System”, 1st IFIP Wireless Days, 2008, pp. 1-5.
[10] H. Reza and N. Mazumder, “A Secure Software Architecture for Mobile Computing”, 9th
International Conference on Information Technology- 2012, pp. 566-571.
[11] C. Möckel, “Usability and Security in EU E-Banking Systems: Towards an Integrated
Evaluation Framework”, International Symposium on Applications and the Internet, 2011, pp.
230-233.
[12] A. Carignani, M. De Marco, and C. Rosenthal-Sabroux, “Supporting a multiple channel
architecture design: the UML contribution in a virtual banking environment”, ECIS 2000
Proceedings. Paper 21. pp. 883-888.

7. [13] S. Mitchell, C.J. Pavlovski, et al. “Multimodal Natural Language Platform Supporting
Cellular Phones”, The ACM Journal of Mobile Computing and Communications Review
(MC2R), ACM Sigmobile, Vol.10, Iss.3, pp. 34-45, 2006.
[14] C. Cox, “Trusted Service Manager: The Key to Accelerating Mobile Commerce”, FirstData,
2009. http://www.firstdata.com/downloads/thought-leadership/fd_mobiletsm_whitepaper.pdf
[15] D. Worthington, “Five Reasons Why Banks Should be their own TSMs”, BellId, 2012.
http://www.bellid.com/media1/blog/view/17-5-reasons-why-banks-should-be-their-own-tsms
[16] Business Wire, “U.S. Bank Introduces Augmented Reality iPhone® Application to Find
Branches and ATMs”, 2012.
http://www.businesswire.com/news/home/20120411005193/en/U.S.-Bank-Introduces-
Augmented-RealityiPhone
[17] PrivatBank, “PrivatBank ready for customer service with Google Glass”, Press Release, April
2013. http://www.privatbank.lv/en/presscenter/pressrelease/2013/news2013-04-001/.

8. HYBRID DATABASE SYSTEM FOR BIG DATA
STORAGE AND MANAGEMENT
Rama Devi Ravipati1
and Munther Abualkibash2
1
Graduate student in the Department of Computer Science, Eastern Michigan University,
Ypsilanti, Michigan
2
School of Information Security and Applied Computing, Eastern Michigan University,
Ypsilanti, Michigan
ABSTRACT
Relational database systems have been the standard storage system over the last forty years. Recently,
advancements in technologies have led to an exponential increase in data volume, velocity and variety
beyond what relational databases can handle. Developers are turning to NoSQL which is a non-
relational database for data storage and management. Some core features of database system such as
ACID have been compromised in NOSQL databases. This work proposed a hybrid database system
for the storage and management of extremely voluminous data of diverse components known as big
data, such that the two models are integrated in one system to eliminate the limitations of the
individual systems. The system is implemented in MongoDB which is a NoSQL database and SQL.
The results obtained, revealed that having these two databases in one system can enhance storage and
management of big data bridging the gap between relational and NoSQL storage approach.
KEYWORDS
ACID, BASE, Big data, NoSQL, SQL, MongoDB.
For More Details: http://aircconline.com/ijcsea/V7N4/7417ijcsea02.pdf
Volume Link: http://airccse.org/journal/ijcsea/index.html

9. REFERENCES
[1] Codd, E. F. (1970). “A relational model of data for large shared data banks”,
Communications of the ACM Vol. 13, No. 6, pp 377–387.
[2] Hecht, R., Jablonski, S. (2011) “ NoSQL evaluation: A use case oriented survey” Cloud and
Service Computing (CSC) International Conference. pp336 – 341
[3] Konstantinou, I., Angelou, E., Boumpouka, C., Tsoumakos, D. and Koziris, N. (2011) “On the
elasticity of NoSQL databases over cloud management platforms” In Proceedings of the 20th
ACM international conference on Information and knowledge management (CIKM '11), New
York, NY, USA
[4] Van der Veen, J.S., van der Waaij, B.,Meijer, R.J.(2012) “Sensor Data Storage Performance:
SQL or NoSQL, Physical or Virtual” IEEE 5th International Conference on Cloud Computing
(CLOUD) pp. 431 – 438.
[5] Singh, N., Garg,N.& Mittal,V.(2012). “Big data- Insight, Motivation and Challenges” Internal
Journal of Scientific and Engineering Research, Vol. 4
[6] Schram, A., Anderson, K. M. (2012) “MySQL to NoSQL: data modelling challenges in
supporting scalability” Proceedings of the 3rd annual conference on Systems, programming, and
applications: software for humanity (SPLASH '12). ACM, New York, NY, USA, pp. 191-202.
[7] Indrawan-Santiago, M. (2012). “Database Research: Are We at a Crossroad? Reflection on
NoSQL.” Proceedings of the 15th International Conference on Network-Based Information
Systems, pp 45-51.
[8] Tauro Clarence T.,Patil,Baswanth R., Prashant K.V.(2013). “A comparative analysis of
different NoSQL databases on data model, query model, and replication model” Internal
Conference on Emerging Research in Communication and Application ERCICA. Bangalor India
[9] Nayak, A. ,Poriya ,A.,Dikshay Poojary (2013)” Types of NoSQL databases and its
comparison with relational databases”. International Journal of Applied Information Systems
Vol.5, No. 4 pp 16-19
[10] Grolinger, K.,Higashinow,T. Wari,Capretz,M.AM (2013)”Data Management in cloud
environments: NoSQL and NewSQL data stores” Vo l2. No. 22

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International Journal of Web Information Systems • Vol. 9, No.1 pp 69-82.
[12] Wu, L., Yuan,L., Huai, Y. (2013). “Survey of large scale data management system for big
data applications” Journal of Computer Science and Technology. Vol. 30 pp 163-183.
[13] Moniruzzaman, A .B., Hossain, S.A.(2013). “NoSQL database: New era of databases for big
data analytics-classification, characteristics and comparison” International Journal of
Computer Science and Technology. Vol. 6 No. 4
[14] Nawsher Khan, Ibrar Yaqoob, Ibrahim Abaker Targio Hashem, Zakira Inayat,Waleed
Kamaleldin Mahmoud Ali,Muhammad Alam,Muhammad Shiraz,Abdullah Gani (2014). “Big
Data: Survey, Technologies, Opportunities, and Challenges” The Scientific World Journal
Vol. 2014 , No. 712826
[15] Abramova, V., Bernardino,J., Furtado,P.(2014). “Experimental evaluation of: NoSQL
databases” International Journal of Database Management System .Vol.6, No. 3
[16] Abramova, V., Bernardino,J., Furtado,P.(2014). “Which NoSQL database? A performance
overview” Open Journal of Databases (OJDB). Vol. 1, Issue 2
[17] Wu, L., Yuan, L., You, J. (2014). “BASIC: An alternative to BASE for large scale data
management systems”, https://webdocs.cs.ualberta.ca/~yuan/papers/ieee_bigdata.pdf
[18] Manoj,V.(2014) “Comparative study of NoSQL document, column store databases and
evaluation of Cassandra” International Journal of Database Management Systems ( IJDMS)
Vol.6, No.4
[19] Rakesh, K., Shilpi, C., Somya, B(2015).”Effective way to handling big data problems using
NoSQL database (MongoDB)” Journal of Advanced Database Management & Systems. Vol. 2,
pp 42–48.
[20] Wu,C.M.,Huang,Y.F.,Lee,J.(2015). “Comparisons between MongoDB and MS-SQL databases
on the TWC website” American Journal of Software Engineering and Applications. Vol. 4 No.2
pp 35-41.
[21] GC, Deepak (20016) "A Critical Comparison of NOSQL Databases in the Context of Acid and
Base" Culminating Projects inInformation Assurance. Paper 8.

11. [22] Mukherjee, S.,Shaw R.(2016) “Big data concepts, applications, challenges and future scope”
International Journal of Advanced Research in Computer and Communication Engineering Vol.
5, Issue 2
[23] http://refreshmymind.com/wpcontent/uploads/2016/02/nosql_documents_stores_mongodb
couchdb.png
[24] Albertas, Krisaunas, Benefits of NoSQL. https://www.devbridge.com/articles/benefits-of-
nosql/#
[25] Why NoSQL, http://img.blog.csdn.net/20151001143133915
AUTHORS
Blessing E.James is a Graduate Assistant in the Department of Computer Science ,Akwa Ibom State
University, She obtained a Bachelor of Technology degree in Mathematics and Computer Science,
2010 from the Federal University of Technology Owerri, Imo State, Nigeria and a Master of Science
degree (2007) from the University of Port Harcourt, Nigeria. Her interest is on Big data, Database
Management Systems, Database Knowledge Management, Data Mining and Machine Learning.
Prince Oghenekaro ASAGBA had his B.Sc degree in Computer Science at the University of Nigeria,
Nsukka, in 1991 with a Second Class (Hons) Upper Division.He had his M.Sc degree in Computer
Science at the University of Benin in April, 1998, and a Ph.D degree in Computer Science at the
University of Port Harcourt in March, 2009. Asagba is an avid researcher with over fifty researched
published articles in reputable journals, both locally and internationally. Asagba possesses over 20
years of reasonable wealth of research / teaching experience at the University level. He is a Visiting
Professor / Scholar to some Universities in Nigeria. His research interest include: Computing and
Information Security, Network Analysis, Software Engineering, Database Management Systems,
Modelling and Programming. He is a member of Nigeria Computer Society (NCS) and Computer
Professional Registration Council of Nigeria (CPN).

12. DESIGN AND DEVELOPMENT OF AUTOMATIC WATER
FLOW METER
Ria Sood1
, Manjit Kaur2
, Hemant Lenka3
1,2
Academic and Consultancy Services-Division
Centre for Development of Advanced Computing(C-DAC), Mohali, India
3
Digital Electronics and Communication Division (DECD)
Centre for Development of Advanced Computing(C-DAC), Mohali, India
ABSTRACT
Effective irrigation water management begins with timing and regulating irrigation water application
in a way that will satisfy the need of the crop without wasting water, soil and crop nutrients. This
involves supplying water according to the crop requirement, quantity that can be held by the soil and
is available to the crop at rates tolerated according to the soil characteristics. So measuring water in
fields is very essential step in irrigation management systems. There are many water flow
measurement techniques as well as different types of water flow meters used in irrigation to measure
the volume of water flow in pipelines but these all are too costly. This paper describes design and
development of low cost automatic water flow meter which supplies only required amount of water to
the crops saving water as well as energy. G1/2 Hall Effect water flow sensor is used as a sensing unit
with a turbine rotor inside it whose speed of rotation changes with the different rate of flow of water.
The Hall Effect sensor outputs the corresponding pulse train for frequency input to the
microcontroller. The whole system comprises of AT89S52 microcontroller, G1/2 Hall Effect water
flow sensor, relay, optocoupler, a water pump, 5V supply, LCD, keypad and some passive
components. The AT89S52 microcontroller is programmed in Keil development Tool.
KEYWORDS
Low cost sensor, Hall Effect sensor, rotation of rotor, flow rate of water, AT89S52 microcontroller,
Keil software, Ultrasonic flow meter.
For More Details: http://airccse.org/journal/ijcsea/papers/3313ijcsea06.pdf
Volume Link: http://airccse.org/journal/ijcsea/current2013.html

13. REFERENCES
[1] N.R Kolhare, P.R Thorat,(2013) “An Approach of Flow Measurement In Solar Water Heater
Using Turbine Flow Meter,” International Journal of Engineering Research & Technology
(IJERT), Vol. 2, pp. 1-4.
[2 ]Luis Castaiier, Vicente Jimenez, Manuel Dom'nguez, Francesc Masana and Angel
Rodriguez,(1997) “Design and fabrication of a low cost water flow meter”, IEEE International
Conference on Solid-State Sensors and Actuators, Vol. 5, pp. 159-162. Digital Object Identifier:
10.1109/SENSOR.1997.613607
[3] Shiqian Cai and Haluk Toral, (1993) “Flowrate Measurement in Air-Water Horizontal Pipeline by
Neural Networks,” International Joint Conference on Neural Networks, pp.2013-2016.
[4] Santhosh KV and BK Roy,(2012) “An Intelligent Flow Measurement Technique using
Ultrasonic Flow Meter with Optimized Neural Network,” International Journal of Control
and Automation, Vol.5, pp. 185- 196.
[5] Young-Woo Lee, Seongbae Eun, Seung-Hyueb Oh,(2008) “Wireless Digital Water Meter with
Low Power Consumption for Automatic Meter Reading,” International Conference on
Convergence and Hybrid Information Technology IEEE, pp. 639-645. DOI 10.1109/ICHIT.19
/2008.172.
[6] Javad Rezanejad Gatabi, Farshid Forouzbakhsh, HadiEbrahimi Darkhaneh, Zahra Rezanejad
Gatabi, Majid Janipour, Iman Rezanejad Gatabi,(2010) “Auxillary Fluid Flow Meter,” European
Journal of Scientific Research, Vol. 42 , pp. 84-92.
[7] Zhang Wenzhao, Liu Zhizhuang, Xu Xiao, Liu Ailing, Chen Aiwu,(2010), “A Liquid DP Flow
Sensor on Straight Pipe,” International Conference on Industrial Mechatronics and Automation,
Vol. 1, pp.481-485. Digital Object Identifier :10.1109/ICINDMA.2010.5538180.
[8] Thwe Mu Han, Ohn Mar Myaing, “Design and Construction of Microcontroller-Based Water
Flow Control System, ” International Conference on Circuits, System and Simulation, Vol. 7,
pp. 304-309.
[9] Gang-Li Qiao-Zhen Feng Dong,(2006) “Study on wide range turbine flow meter”, Proceedings of
the Fifth International Conference on Machine Learning and Cybernetics IEEE, pp. 775- 778.

14. [10] Enggcyclopedia, “Turbine Flow meters” Available:
http://www.enggcyclopedia.com/2012/01/turbineflow-meters/
[11] AKM semiconductors,“Hall Effect sensor application guide” pp 1-1. Available:
http://www.akm.com/Brochures/HallSensortechnicalguide.pdf.
12] N.R Kolhare, P.R Thorat,(2013) “An Approach of Flow Measurement In Solar Water Heater
Using Turbine Flow Meter,” International Journal of Engineering Research & Technology
(IJERT), Vol. 2, pp. 1-4.
AUTHORS
Ria Sood1 received her Bachelor of technology in Electronics and Communication Engineering from
Lovely Professional University (LPU), Phagwara, Punjab, India and persuing Masters of Technology
in Embedded systems from Centre for development for Advanced Computing (C-DAC), Mohali,
India. Her research interest include embedded system design with advanced microcontrollers and DSP
processors, Analog and Digital electronics.
Manjit Kaur2 received her Bachelor of Technology in Electronics and
Communication Engineering from Beant College of Engineering & Technology
(BCET), Gurdaspur, Punjab, India and Masters of Technology in
Microelectronics from Punjab University, Chandigarh, India. Presently she is
serving as Engineer at Centre for Development of Advanced Computing
(CDAC), Mohali, India. This institute is a scientific society of the Ministry of Communications &
Information Technology, Govt. of India, involved in Research and Development with an objective to
create focus on Advanced Information Technologies, High-end Academics & Training relevant to R
& D societies. She is the core faculty of M.Tech (VLSI Design) with an experience of 5+ years in
teaching. Alongside she has been pursuing pioneering research on cutting edge topics in the field of
VLSI design. Her research interests include Semiconductor Device Physics & Modeling, Advanced
VLSI Design & Testing Methodologies, ASIC & FPGA Design Techniques and Low Power VLSI
Design.
Hemant Kumar Lenka3 received his Bachelor of Technology in Electronics and Communication
Engineering from A.M.I.E and Masters of Technology in I.T from Rajasthan Deemed University in
July 2005. His area of interest includes AutoCAD Autolisp programming and Customisation, Cad
tools for PCB design.

15. WORD AND SENTENCE LEVEL EMOTION ANALYZATION IN
TELUGU BLOG AND NEWS
Sadanandam. Manchala1
, D. Chandra Mohan2
and A. Nagesh3
1
Assistant professor of CSE KUCE&T, Kakatiya University Campus (KU)
Warangal, Andhra Pradesh, India.
2
Assistant professor of CSE Matrix Institute of Technology&Engg.,
Hyderbadad, Andhra Pradesh, India.
3
Assoc. Professor of CSE Mahatma Gandhi Institute of Technology
Hyderabad , Andhra Pradesh, India.
ABSTRACT
Emotion analysis, a recent sub discipline at the crossroads of information retrieval and computational
linguistics is becoming increasingly important from application viewpoints of affective computing.
Emotion is crucial to identify as it is not open to any objective observation or verification. In this
paper, emotion analysis on blog texts has been carried out for a less privileged language, Telugu and
the same system has been applied on the English SemEval 2007 affect sensing corpus containing only
news headlines. A set of six emotion tags, namely, happy ( ), sad ( ), anger ( ), fear ( ), surprise ( )and
disgust ( ), have been selected towards this emotion detection task for reliable and semi-automatic
annotation of blog and news data. Conditional Random Field (CRF) based classifier has been applied
for recognizing six basic emotion tags for different words of a sentence. The classifier accuracy has
been improved by arranging an equal distribution of emotional tags and non-emotional tag. A score
based technique has been adopted to calculate and assign tag weights to each of the six emotion tags.
A sense based scoring strategy has been applied to identify sentence level emotion scores for the six
emotion tags based on the acquired word level emotion tags. Sentence level emotion tagging has been
carried out based on the maximum obtained sentence level emotion scores. Evaluation has been
conducted for each emotion class separately on 200 test sentences from each of the Telugu blog and
English news data. The system has resulted accuracies of 69.82% and 71.06% for happy, 70.24% and
66.42% for sad, 65.73% and 64.27% for anger, 76.01% and 69.90% for disgust, 72.19% and 73.59%
for fear and 70.54% and 66.64% for surprise emotion classes on blog and news test data respectively.
KEYWORDS

16. SentiWordNet, Blog, News, Emotion, CRF, Emotion tag, happy, sad, anger, fear, disgust, surprise.
For More Details: http://airccse.org/journal/ijcsea/papers/2312ijcsea17.pdf
Volume Link: http://airccse.org/journal/ijcsea/current2012.html
REFERENCES
[1] Carlo Strapparava, RadaMihalcea.: SemEval-2007 Task 14: Affective Text.Proceedings of the
45th Aunual Meeting of Association for Computational linguistics (2007)
[2] Paul Ekman.: Facial expression and emotion. vol. 48(4), pp. 384--392. American Psychologist
(1993)
[3] Carlo Strapparava and Alessandro Valitutti.: WordNet-Affect: an affective extension of
WordNet. In Proceedings of the 4th International Conference on Language Resources and
Evaluation, pp. 1083--1086 (2004)
[4] Andrea Esuli and FabrizioSebastiani.: SENTIWORDNET: A Publicly Available Lexical
Resource for Opinion Mining. LREC-06 (2006)
[5] Andrew McCallum, Fernando Pereira and John Lafferty.: Conditional Random Fields:
Probabilistic Models for Segmenting and labeling Sequence Data. ISBN, pp. 282 -- 289
(2001)
[6] Peter D. Turney.: Thumbs Up or Thumbs Down? Semantic Orientation Applied to
Unsupervised Classification of Reviews. Proceedings of the 40th Annual Meeting of the
Association for Computational Linguistics (ACL), pp. 417-- 424 (2002)
[7] Bo Pang and Lillian Lee and ShivakumarVaithyanathan.: Thumbs up? Sentiment Classification
using Machine Learning Techniques. Proceedings of the Conference on Empirical Methods in
Natural Language Processing (EMNLP), pp. 79--86 (2002)
[8] FabrizioSebastiani.: Machine learning in automated text categorization. ACM Computing
Surveys, vol. 34(1) (2002)
[9] G. Mishne and M. de Rijke.: Capturing Global Mood Levels using Blog Posts, Proceedings of
AAAI, Spring Symposium on Computational Approaches to Analysing Weblogs, 145--152
(2006)
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