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Reseach Article

Sentiment Analysis of Amazon Canon Camera Review using Hybrid Method

by Indrajeet Kaur Chhabra, Gend Lal Prajapati
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 5
Year of Publication: 2018
Authors: Indrajeet Kaur Chhabra, Gend Lal Prajapati
10.5120/ijca2018917545

Indrajeet Kaur Chhabra, Gend Lal Prajapati . Sentiment Analysis of Amazon Canon Camera Review using Hybrid Method. International Journal of Computer Applications. 182, 5 ( Jul 2018), 25-28. DOI=10.5120/ijca2018917545

@article{ 10.5120/ijca2018917545,
author = { Indrajeet Kaur Chhabra, Gend Lal Prajapati },
title = { Sentiment Analysis of Amazon Canon Camera Review using Hybrid Method },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2018 },
volume = { 182 },
number = { 5 },
month = { Jul },
year = { 2018 },
issn = { 0975-8887 },
pages = { 25-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number5/29759-2018917545/ },
doi = { 10.5120/ijca2018917545 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:10:28.561469+05:30
%A Indrajeet Kaur Chhabra
%A Gend Lal Prajapati
%T Sentiment Analysis of Amazon Canon Camera Review using Hybrid Method
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 5
%P 25-28
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sentiment Analysis or Opinion Mining is a new developing research field that has opened new challenges for researchers to be answered. Sentiment analysis or Opinion mining is a very important field in finding the correct sentiment of customer product review, election result analysis, summarization of news articles. Sentiment analysis has opened a new door in different domains like financial, telecommunication, business, medical, social events, and e-shopping. In this paper, a hybrid sentiment analysis approach is proposed to analyze “Amazon” Canon camera reviews and classify them into positive and negative polarity classes which is useful for other customers and organizations to take future decisions. The results of hybrid approach show improvement in accuracy, and also in precision and recall measures.

References
  1. Addlight Mukwazvure, K.P Supreethi, 2015. A Hybrid Approach to Sentiment Analysis of News Comments.2015 IEEE.
  2. Yoonjung Choi, Youngho Kim, Sung-Hyon Myaeng, 2009, November. Domain-Specific Sentiment Analysis using Contextual Feature Generation.2009 ACM.
  3. Khin Phyu Phyu Shein , Thi Thi Soe Nyunt 2010. Sentiment Classification based on Ontology and SVM Classifier. In 2010 Second International Conference on Communication Software and Networks, 2010 IEEE.
  4. Brett Duncan and Yanqing Zhang,2015. Neural Networks for Sentiment Analysis on Twitter. In 14th International Conference on Cognitive Informatics & Cognitive Computing,2015 IEEE.
  5. Taysir Hassan A. Soliman, Mostafa A. Elmasry et al., 2012, October. Utilizing Support Vector Machines in Mining Online Customer reviews. ICCTA 2012 IEEE.
  6. Rincy Jose, Varghese S Chooralil, 2015, November. Prediction of Election Results by Enhanced Sentiment Analysis on Twitter Data using Word Sense Disambiguation. In International Conference on Control, Communication & Computing India (ICCC) 2015 IEEE.
  7. Prabu Palanisamy, Vineet Yadav and Harsha Elchuri, 2013, June. Serendio: Simple and Practical Lexicon Based Approach to Sentiment Analysis. In Second Joint Conference on Lexical and Computational Semantics (*SEM),2013 Association of Computer Linguistic (Vol. 2, pp. 543-548).
  8. Manju Venugopalan, Deepa Gupta, 2015.Exploring Sentiment Analysis on Twitter Data. 2015 IEEE.
  9. Chetana Pujari, Aishwarya and Nisha P. Shetty, 2018. Comparison of Classification Techniques for Feature Oriented sentiment analysis of product review data. In Data Engineering and Intelligent Computing, Advances in Intelligent Systems and Computing 542, Springer Nature Singapore Pte Ltd. 2018 Springer.
  10. Mrs. R. Nithya, Dr. D. Maheshwari, 2014. Sentiment Analysis on Unstructured review. In International Conference on Intelligent Computing Applications,2014 IEEE.
  11. Santhosh Kumar K L, Jayanti Desai, Jharna Majumdar, 2016. Opinion Mining and Sentiment Analysis on online customer review.2016 IEEE.
  12. Orestes Appel, Francisco Chiclana, Jenny Carter and Hamido Fujita,2016. A Hybrid Approach to Sentiment Analysis. 2016 IEEE.
  13. A.B. Pawar, M.A. Jawale and D.N. Kyatanavar, 2016. Fundamentals of Sentiment Analysis: Concepts and Methodology, Sentiment Analysis and Ontology Engineering, Studies in Computational Intelligence 639,2016 Springer.
  14. Shirin Noekhah, Naomie Binti Salim and Nor Hawaniah Zakaria, 2018. A Comprehensive Study on Opinion Mining Features and Their Applications. In Recent Trends in Information and Communication Technology, 2018 Springer.
  15. Bhavitha B K, Anisha P Rodrigues, Dr. Niranjan N Chiplunkar, 2017. Comparative Study of Machine Learning Techniques in Sentiment Analysis. In International Conference on Inventive Communication and Computational Technologies (ICICCT), 2017, IEEE.
  16. Xing Fang and Justin Zhan,2015. Sentiment analysis using product review data. Journal of Big Data 2015 Springer.
  17. Yoonjung Choi, Janyce Wiebe and Rada Mihalcea, 2017, June. Coarse-grained +/-Effect Word Sense Disambiguation for Implicit Sentiment Analysis. IEEE (2017)
  18. A. Eesee and N. Omar, 2016, April. A Hybrid Method for Arabic Educational Sentiment Analysis. 2016 Journal of Applied Sciences.
  19. Bissan Ghaddar, Joe Naoum-Sawaya, 2017, August. High Dimensional Data Classification and Feature Selection Using Support Vector Machine. 2017 European Journal of Operational Research.
  20. Reinald Kim Amplayo, Min Song, 2017, March. An adaptable fine-grained sentiment analysis for summarization of multiple short online reviews. 2017 Data & Knowledge Engineering.
Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Analysis Opinion Mining Feature Extraction Machine Learning Support Vector Machine Amazon