CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Adaptive Product Review Classification Engine (APRCE) for Social Network Product Review Evaluation

by Anjani Chanji, Rekha Bhatia
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 7
Year of Publication: 2016
Authors: Anjani Chanji, Rekha Bhatia
10.5120/ijca2016910889

Anjani Chanji, Rekha Bhatia . Adaptive Product Review Classification Engine (APRCE) for Social Network Product Review Evaluation. International Journal of Computer Applications. 146, 7 ( Jul 2016), 41-45. DOI=10.5120/ijca2016910889

@article{ 10.5120/ijca2016910889,
author = { Anjani Chanji, Rekha Bhatia },
title = { Adaptive Product Review Classification Engine (APRCE) for Social Network Product Review Evaluation },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 7 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 41-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number7/25414-2016910889/ },
doi = { 10.5120/ijca2016910889 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:49:49.370785+05:30
%A Anjani Chanji
%A Rekha Bhatia
%T Adaptive Product Review Classification Engine (APRCE) for Social Network Product Review Evaluation
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 7
%P 41-45
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The product review classification is the process of automatic categorization of the review data based upon the specific products. The product review classification helps the techniques for analysis of the product reviews and to classify them according to the polarity evaluation. The product review classification has been utilized for the comparative analysis of the two similar products to facilitate the customers to make their decision on the basis of the public opinion and choice. In this paper, the product review classification model has been implemented with the supervised modeling, which evaluates the multiple keyword lists for the evaluation of the polarity across the input review data for the different products of the similar categories. The proposed model performance has been evaluated in thevarious domains such as text processing errors, compression types, recall, precision, polarization accuracy, etc. The proposed model has been found efficient in the terms of all of the performance parameters in comparison with the existing models.

References
  1. Che, Wanxiang, Yanyan Zhao, HongleiGuo, Zhong Su, and Ting Liu. "Sentence Compression for Aspect-Based Sentiment Analysis." Audio, Speech, and Language Processing, IEEE/ACM Transactions on 23, no. 12 (2015): 2111-2124.
  2. Liu, Siyuan, Xiaoyin Cheng, and Fan Li. "TASC: Topic-Adaptive Sentiment Classification on Dynamic Tweets." (2015).
  3. Esuli, Andrea, and FabrizioSebastiani. "Sentiwordnet: A publicly available lexical resource for opinion mining." In Proceedings of LREC, vol. 6, pp. 417-422. 2006.
  4. Kim, Soo-Min, and Eduard Hovy. "Automatic identification of pro and con reasons in online reviews."In Proceedings of the COLING/ACL on Main conference poster sessions, pp. 483-490.Association for Computational Linguistics, 2006.
  5. Hu, Minqing, and Bing Liu. "Mining and summarizing customer reviews."InProceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 168-177.ACM, 2004.
  6. Jin, Jian, Ping Ji, and Ying Liu. "Translating online customer opinions into engineering characteristics in QFD: A probabilistic language analysis approach." Engineering Applications of Artificial Intelligence 41 (2015): 115-127
  7. Suanmali, Ladda, NaomieSalim, and Mohammed Salem Binwahlan. "Fuzzy logic based method for improving text summarization." arXiv preprint arXiv:0906.4690 (2009).
  8. Ghorashi, Seyed Hamid, Roliana Ibrahim, ShirinNoekhah, and NiloufarSalehiDastjerdi. "A frequent pattern mining algorithm for feature extraction of customer reviews." In IJCSI International Journal of Computer Science Issues. 2012.
  9. Yazhini, R., and Raja P. Vishnu. "Automatic summarizer for mobile devices using sentence ranking measure."In Recent Trends in Information Technology (ICRTIT), 2014 International Conference on, pp. 1-6.IEEE, 2014.
  10. Zha, Zheng-Jun, Jianxing Yu, Jinhui Tang, Meng Wang, and Tat-Seng Chua. "Product aspect ranking and its applications." Knowledge and Data Engineering, IEEE Transactions on 26, no. 5 (2014): 1211-1224.
  11. Jin, Jian, Ping Ji, and Ying Liu. "Prioritising engineering characteristics based on customer online reviews for quality function deployment." Journal of Engineering Design 25, no. 7-9 (2014): 303-324.
  12. Kherwa, Pooja, AnishSachdeva, DhruvMahajan, NishthaPande, and Praveen Kumar Singh. "An approach towards comprehensive sentimental data analysis and opinion mining."In Advance Computing Conference (IACC), 2014 IEEE International, pp. 606-612.IEEE, 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Product reviews classification Polarization Sentence Compression Review Classification.

Powered by PhDFocusTM