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Reseach Article

Review: Sentiment Analysis using SVM Classification Approach

by Shweta V. Raut, Madhu M. Nashipudimath
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 37
Year of Publication: 2019
Authors: Shweta V. Raut, Madhu M. Nashipudimath
10.5120/ijca2019917993

Shweta V. Raut, Madhu M. Nashipudimath . Review: Sentiment Analysis using SVM Classification Approach. International Journal of Computer Applications. 181, 37 ( Jan 2019), 1-8. DOI=10.5120/ijca2019917993

@article{ 10.5120/ijca2019917993,
author = { Shweta V. Raut, Madhu M. Nashipudimath },
title = { Review: Sentiment Analysis using SVM Classification Approach },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2019 },
volume = { 181 },
number = { 37 },
month = { Jan },
year = { 2019 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number37/30271-2019917993/ },
doi = { 10.5120/ijca2019917993 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:08:22.547432+05:30
%A Shweta V. Raut
%A Madhu M. Nashipudimath
%T Review: Sentiment Analysis using SVM Classification Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 37
%P 1-8
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recently, lots of attempts are done to work on social sites to examine of public sentiment. Most of the efforts are usable to give fine ideas of social public opinions from social media. Hence, there is a need of suitable approach to overcome this problem. Sentiment Analysis (SA) is an action of computationally diagnosing and grouping opinions represented in a particular bunch of text. It is used to recognize opinion of public as feedbacks depending upon the data/domain in social media. Information Gain (IG) is a measure used to identify most impactful words as features in the tweet to classify the opinions using some classification approaches. The purpose of this article is to discuss some approaches for extracting features from tweets and classifying it.

References
  1. Mika V. Mantyla, Daniel Graziotin, Miikka Kuutila, “The Evolution of Sentiment Analysis – A review of Research Topics, Venues, and Top cited Papers” , Feb 2018, ISSN 1574-0137, https://doi.org/10.1016/j.cosrev.2017.10.002, University of Oulu, Finland.
  2. Devika MD, Sunitha C, Amal Ganesh ,“Sentiment Analysis: A Comparative Study On Different Approaches” , Fourth International Conference on Recent Trends in Computer Science and Engineering, 2016, doi:10.1016/j.procs.2016.05.124, Chennai, Tamil Nadu, India.
  3. Big data and data protection, 20140728 Version: 1.0
  4. Michael, Katina, and Keith W. Miller. "Big data: New opportunities and new challenges [guest editors' introduction]." Computer 46.6 (2013): 22-24.
  5. Mona Tanwar, Reena Duggal, Sunil Kumar Khatri, “Unravelling Unstructured Data: A Wealth of Information in Big Data”, IEEE , 2015 , Noida, India.
  6. Akash, Bryan, Nishi, Prashant, Subhadeep and Vaibhav,v “Emerging Marketing Analytics- Big Data.” [Online]. Available: http://www.slideshare.net/AkashTyagi8/big-data-marketinganalytics
  7. K. Cukier, “Data, data everywhere: A special report on managing information,” The Economist, February 25, 2010.
  8. Amir Gandomi and Murtaza Haider, “Beyond the hype: Big data concepts, methods, and analytics,” International Journal of Information Management, vol. 35, Issue 2, pp 137-144, April 2015.
  9. Bing Liu. Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers, May 2012.
  10. Ashish Katrekar, AVP, Big Data Analytics , “An Introduction to Sentiment Analysis”, www.globallogic.com.
  11. Prachi Bansal and Ramanjot Kaur,“Twitter Sentiment Analysis using Machine Learning and Optimization Techniques”, Feb 2018 in International Journal of Computer Applications(0975-8887), Volume 179- No. 19, pp. 5-8, Doaba Institute of Engg. & Technology, Kharar, Punjab, India.
  12. Aldo Hernandez-Suarez, Gabriel Sanchez-Perez, Karina Toscano-Medina, Victor Martinez-Hernandez, Hector Perez-Meana, Jesus Olivares-Mrcaso and Victor Sanchez, “Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regulation”, March 2018 in Preprints(www.preprints.org), Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico.
  13. T.D.V. Kiran*, K. Gowtham Reddy, Jagadeesh Gopal, “Twitter sentiment analysis of game reviews using machine learning techniques”, Journal of Chemical and Pharmaceutical Sciences, Print ISSN:0974-2115,2017, Tamil Nadu, India.
  14. Akshay Amolik, Niketan Jivane, Mahavir Bhandari, Dr. M. Venkatesan, “Twitter Sentiment Analysis of Movie Reviews using Machine Learning Techniques”, e-ISSN:0975-4024, International Journal of Engineering and Technology (IJET), VIT University, Vellore-632014, Tamilnadu, India.
  15. Ana Tarano (atarano) and Dana Murphy (dkm0713), “Tracking #metoo on Twitter to Predict Engagement in the Movement,” 2017.
  16. Munir Ahmad, Shabib Aftab and Iftikhar Ali, “Sentiment Analysis of Tweets using SVM”, International Journal of Computer Applications, Nov 2017(0975-8887), Volume 177- No. 5, Pakistan.
  17. Ankit Pradeep Patel, Ankit Vithalbhai Patel, Sanjaykumar Ghanshyambhai Butani and Prashant B. Sawant, “Literature Survey on Sentiment Analysis of Twitter Data using Machine Learning Approaches” in International Journal for Innovative Research in Science & Technology, ISSN:2349-6010, Volume 3, Issue 10, March 2017, Mumbai, India.
  18. Alnashwan, Rana; O’Riordan, Adrian P.; Sorensen, Humphrey; Hoare, Cathal, “Improving sentiment analysis through ensemble learning of meta-level features”, Sept. 2016, Proceedings of the 2nd International Workshop on Knowledge Discovery on the WEB, Cagliari, Italy, CEUR Workshop Proceedings, 1748.
  19. Pooja C. Sangvikar, “A Survey on Sentiment Analysis and Opinion Mining”, International Journal for Scientific Research & Development, Vol. 3, Issue 10, 2015, ISSN(online):2321-0613, Pune, India.
  20. Ritwik Moghe, Pranita Khandelwal, Raju Bhattachayya and Sushant Rajput, “Studying Twitter Sentiment of Football Superstars”, 2015, CDS Group-2.
  21. P. Kalarani and Dr. S. Selva Brunda,“User Influential Level Based Sentiment Analysis using User Average Opinion (UAO) and Module Average Opinion(MAO)”, International Journal of Pure and Applied Mathematics, Volume 118, No. 7, 2018, pp. 245-251, ISSN: 1311-8080(printed version); 1314-3395 (on-line version), Tamilnadu, India.
  22. Asriyanti Indah Pratiwi and Adiwijaya, “On the Feature Slection and Classification Based on Information Gain for Document Sentiment Analysis”, Hindawi, Applied Computational Intelligence and Soft Computing, Volume 2018, Article ID 1407817, Feb. 2018, Indonesia.
  23. Shubham Goyal , “Sentiment Analysis of Twitter Dats using Text Mining and Hybrid Classification Approach”, International Journal of Advance Research, Ideas and Innovations in Technology, ISSN: 2454-132X, Vol. 2, Issue 5, Bhwainigarh, Punjab, India, 2016.
  24. Kim Schouten, Flavius Fransincar. And Rommert Dekker, “An Information Gain-Driven Feature Study for Aspect-Based Sentiment Analysis”,Erasmus University Rotterdam, the Netherlands {schouten, frasincar, rdekker}@ese.eur.nl
  25. David Zimbra, M. Ghiassi and Sean Lee, “Brand-related Twitter Sentiment Analysis using Feature Engineering and the Dynamic Architecture for Artificial Neural Networks”, 2016, 49th Hawaii International Conference on System Sciences, Santa Clara University, IEEE Computer Society.
  26. Shahana P.H, Bini Omman, “Evaluation of Features on Sentiment Analysis”, International Conference on Information and Communication Technologies (ICICT 2014), Kerala.Example when using et al.:
  27. Munir Ahmad, Shabib Aftab, Muhammad Salman Bashir, Noureen Hameed, “Sentiment Analysis using SVM: A Systematic Literature Review”, International Journal of Advanced Computer Science and Applications (www.ijacsa.thesai.org) ”, Pakistan, 2018.
  28. Soumil Mandal, Dipankar Das, “Analyzing Roles of Classifiers and Code-Mixed factors for Sentiment Identification”, 15 March 2018, arXiv:1801.02581v2 [cs.CL].
  29. Harnani Mat Zin, Norwati Mustapha, Masrah Azrifah Azmi Murad and Nurfadhlina Mohd Sharef, “The effects of pre-processing strategies in sentiment analysis of online movie reviews”, 2017, American Institute of Physics, doi:10.1063/1.5005422.
  30. Shruti Gupta, Ashutosh Pandey, Prof. K.K. Paliwal, “Sentiment Analysis of Twitter and Facebook Data Using Map-Reduce”, IJEE, A UGC Recommended Journal, June 2017, Panipat, India, www.csjournals.com
  31. Ali Mustafa Qamar, Suliman A. Alsuhibany, and Syed Sohail Ahmed, “Sentiment Classification of Twitter Data Belonging to Saudi Arabian Telecommunication Companies” , International Journal of Advanced Computer Science and Applications (IJACSA) , 2017, Saudi Arabia.
  32. LI Bing, Keith C.C. Chan, “A Fuzzy Logic Approach for Opinion Mining on Large Scale Twitter Data”, 2014, IEEE/ACM 7th International Conference on Utility and Cloud Computing, Hong Kong.
  33. Soumil Mandal, Sainik Kumar Mahata, Dipankar Das, “Preparing Bengali-English Code-Mixed Corpus for Sentiment Analysis of Indian Languages”, Kolkata, arXiv:1803.04000v1 [cs.CL] 11 March 2018.
  34. Gautami Tripathi and Naganna S., “Feature Selection and Classification Approach For Sentiment Analysis”, Machine Learning and Applications: An International Journal (MLAIJ), June 2015.
  35. Vishal A. Kharde and S.S. Sonawane, “Sentiment Analysis of Twitter Data: A Survey of Techniques”, International Journal of Computer Applications (0975-8887), April 2016, Pune.
  36. Mondher Bouazizi and Tomoaki Otsuki (Ohtsuki), ( Senior Member, IEEE), “A Pattern-Based Approach for Sarcasm Detection on Twitter”, Japan, September 28,2016.
  37. Mondher Bouazizi and Tomoaki Ohtsuki, “Opinion Mining in Twitter - How to make use of Sarcasm to Enhance Sentiment Analysis” , 2015, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Japan.
  38. David Zimbra, M. Ghiassi and Sean Lee, “Brand-Related Twitter Sentiment Analysis using Feature Engineering and the Dynamic Architecture for Artifial Neural Networks”, 2016 49th Hawaii International Conference on System Sciences.
  39. Daniel Jurafsky and James H. Martin, “Chapter 6:Naïve Bayes and Sentiment Classification”, Speech and Language Processing, August 7,2017.
  40. L. Almuqren and A. I. Cristea, “Twitter analysis to predict the satisfaction of telecom company customers,” in Late-breaking Results, Demos, Doctoral Consortium, Workshops Proceedings and Creative Track of the 27th ACM Conference on Hypertext and Social Media (HT 2016), Halifax, Canada, July 13-16, 2016. ---> work will be completed by 2022.
  41. Yun Wan and Dr. Qigang Gao, “An Ensemble Sentiment Classification System of Twitter Data for Airline Services Analysis”, Canada, 2015 IEEE 15th International Conference on Data Mining Workshops.
  42. Anastasia Giachanou and Fabio Crestani, “Like It or Not: A Survey of Twitter Sentiment Analysis Methods”, Svizzera Italiana, 12 April 2018, ACM Computing Surveys.
  43. Rucha Jadhavar, Agastya Kumar Komarraju, “Sentiment Analysis of Netfix and Competitor Tweets to Classify Customer Opinions”, Paper 2708-2018.
  44. Mamatha M, Thriveni J, Nenugopal K. R., “Techniques of Sentiment Classification, Emotion Detection, Feature Extraction and Sentiment Analysis: A Comprehensive Review”, International Journal of Computer Sciences and Engineering (IJCSE), E-ISSN: 2347-2693,2018.
Index Terms

Computer Science
Information Sciences

Keywords

Feature Selection Support Vector Machine(SVM) Information Gain(IG) Sentiment Analysis Twitter.