Open Access   Article Go Back

Intelligent Opinion Polarity and Analysis in Discovering Product Related Customer Reviews: A Survey

Karuna Sahay1 , Kaptaan Singh2 , Amit Saxena3

Section:Survey Paper, Product Type: Journal Paper
Volume-7 , Issue-6 , Page no. 1139-1143, Jun-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i6.11391143

Online published on Jun 30, 2019

Copyright © Karuna Sahay, Kaptaan Singh, Amit Saxena . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Karuna Sahay, Kaptaan Singh, Amit Saxena, “Intelligent Opinion Polarity and Analysis in Discovering Product Related Customer Reviews: A Survey,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1139-1143, 2019.

MLA Style Citation: Karuna Sahay, Kaptaan Singh, Amit Saxena "Intelligent Opinion Polarity and Analysis in Discovering Product Related Customer Reviews: A Survey." International Journal of Computer Sciences and Engineering 7.6 (2019): 1139-1143.

APA Style Citation: Karuna Sahay, Kaptaan Singh, Amit Saxena, (2019). Intelligent Opinion Polarity and Analysis in Discovering Product Related Customer Reviews: A Survey. International Journal of Computer Sciences and Engineering, 7(6), 1139-1143.

BibTex Style Citation:
@article{Sahay_2019,
author = {Karuna Sahay, Kaptaan Singh, Amit Saxena},
title = {Intelligent Opinion Polarity and Analysis in Discovering Product Related Customer Reviews: A Survey},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2019},
volume = {7},
Issue = {6},
month = {6},
year = {2019},
issn = {2347-2693},
pages = {1139-1143},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4696},
doi = {https://doi.org/10.26438/ijcse/v7i6.11391143}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i6.11391143}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4696
TI - Intelligent Opinion Polarity and Analysis in Discovering Product Related Customer Reviews: A Survey
T2 - International Journal of Computer Sciences and Engineering
AU - Karuna Sahay, Kaptaan Singh, Amit Saxena
PY - 2019
DA - 2019/06/30
PB - IJCSE, Indore, INDIA
SP - 1139-1143
IS - 6
VL - 7
SN - 2347-2693
ER -

VIEWS PDF XML
388 316 downloads 139 downloads
  
  
           

Abstract

Due to rapid advancements in Social media consumer interactions are increasing at faster rate. Twitter has now a days become a social media platform for industries, individuals, educational institutes and organizations who have a strong educational, political, industrial, social, banking or economic concern in maintaining and enhancing their social status and reputation. Posts are generally composed of poorly structured, incomplete, and noisy sentences, irregular expressions, non-dictionary terms, and ill-formed words. The problem is some customers given rating contrast with their comments. The other reviewers must read many comments and comprehend the comments that are different from the rating. Opinion Mining is the computational detailed investigation of people’s attitudes, opinions, and emotions concerning of issues, events, topics or individuals. This paper represents the survey of customer feelings related to online product with their opinion polarity and analysis.

Key-Words / Index Term

Sentiment analysis, Opinion mining, Machine learning, Social Media, Support Vector Machine, Sentiment Polarity

References

[1] Wararat Songpan, The Analysis and Prediction of Customer Review Rating Using Opinion Mining, IEEE SERA 2017, pp. 71-77
[2] Arno Scharl, David Herring, Walter Rafelsberger, Alexander Hubmann-Haidvogel, Ruslan Kamolov, Daniel Fischl, Michael Föls, and Albert Weichselbraun, “Semantic Systems and Visual Tools to Support Environmental Communication”, IEEE SYSTEMS JOURNAL, VOL. 11, NO. 2, JUNE 2017, pp. 762-772
[3] ANH-DUNG VO , QUANG-PHUOC NGUYEN , AND CHEOL-YOUNG OCK, Opinion-Aspect Relations in Cognizing Customer Feelings via Reviews, IEEE 2018, Vol-6, pp.5414-5426
[4] Kamps, J., Marx, M., Mokken, R. J.Using WordNet to Measure Semantic Orientation of Adjectives. LREC 2004. Volume IV, pp. 1115-1118.
[5] Andreevskaia, A., Bergler, S., Urseanu, M.All Blogs Are Not Made Equal: Exploring Genre Di_erences in Sentiment Tagging of Blogs. International Conference on Weblogs and Social Media (ICWSM-2007), Boulder, CO. 2007.
[6] Vandana V. Chaudhari*, Chitra A. Dhawale** and Sanjay Misra,“ Sentiment Analysis Classification: A Brief Review”, I J C T A, 9(23) 2016, pp. 447-454
[7]ANH-DUNG VO , QUANG-PHUOC NGUYEN , AND CHEOL-YOUNG OCK, “Opinion_Aspect Relations in Cognizing Customer Feelings via Reviews”, IEEE 2017, pp. 5415-5427
[8]ATHIRA U, AND SABU M. THAMPI, “Linguistic Feature Based Filtering Mechanism for Recommending Posts in a Social Networking Group”, IEEE 2018, pp. 4469-4484
[9] S. 1. Wu, R.D. Chiang and Z.H. Ji, Development of a Chinese opinion mining system for application to Internet online forum, The Journal of Supercomputing, Springer US[Online], 2016.
[10] Z. Li, L.Liu and C.Li, Analysis of customer satisfaction from Chinese reviews using opinion mining, Proceeding of the 6th IEEE International Conference on Software Engineering and Service Science(ICSESS). 2015, pp.95-99.
[11] Q.Su, X.Xu, H.Guo, Z.Guo, X. Wu, X. Zhang and B.Swen. Hidden Sentiment association in Chinese web opinion mining. Proceeding of the 17th International Conference on World Wide Web, 2008, pp.959-968.
[12] R.M. Duwairi and I. Qarqaz, Arabic Sentiment Analysis using Supervised Classification. Proceeding of 2014 International Conference on Future Internet of Things and Cloud. 2014, pp. 579-583.
[13] H.S. Le, T.V. Le and T.V. Pham, Aspect Analysis for Opinion Mining of Vietnamese Text. Proceeding of International Conference on Advance Computing and Application, 2015, pp.118-123.
[14] V.B. Raut and D.D. Londhe, "Survey on opinion mining and summarization of user review on web", International Journal of Computer Science and Information Technology, Vol. 5(2), 2014, pp. 1026-1030.
[15] Fiaidhi, O. Mohammed, S. Mohammed, S. Fong, and T.H, Kim, Opinion Mining over twiiterspace: Classifying tweets programmatically using the R approach. Proceeding of the 7th International Conference on Digital Information Management, 2012, pp. 313-319.
[16] L. Lin, 1. Li, R. Zhang, W. Yu and C. Sun, Opinion mInIng and sentiment analysis in social networks: A retweeting structure-aware approach. Proceeding of the 7th International Confernece on Utility and Cloud Computing, 2014, pp.890-895.