Aspect Ranking Technique for Efficient Opinion Mining using Sentiment Analysis : Review

Authors(2) :-Prof. Sonali D. Borase, Prof. Prasad P. Mahale

Opinion mining, also called sentiment analysis, is the field of study that analyses peopleís opinions, sentiments, evaluations, appraisals, attitudes, and emotions towards entities such as products, services, organizations, individuals, issues, events, topics, and their attributes. Even though facts still play a very important role when information is sought on a topic, opinions have become increasingly important as well. Opinions expressed in blogs and social networks are playing an important role influencing everything from the products people buy to the presidential candidate they support. Thus, there is a need for a new type of search engine which will not only retrieve facts, but will also enable the retrieval of opinions. Such a search engine can be used in a number of diverse applications like product reviews to aggregating opinions on a political candidate or issue. This paper consist review works have been designed for opinion mining by using classification and ranking techniques.

Authors and Affiliations

Prof. Sonali D. Borase
Assistant Professor, Department of Computer Engineering, NMIMS Mukesh patel school of technology, Shirpur, Maharashtra, India
Prof. Prasad P. Mahale
Assistant Professor, Department of Computer Engineering, SESís R.C Patel institute of technology, Shirpur, Maharashtra, India

Sentiment Analysis, Opinion Mining, POS, Ranking Algorithm, Feature Selection Method, Semantic Orientation.

  1. J. Deshmukh and A. Tripathy, "Entropy based classifier for cross-domain opinion mining", Applied Computing and Informatics, vol. 14, no. 1, pp. 55-64, 2018.
  2. B. Alengadan and S. Khan, "Modified Aspect/Feature Based Opinion Mining for a Product Ranking System", 2018 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), pp. 1-5, 2018.
  3. D. Nyaung and T. Lai Thein, "Feature-Based Summarizing and Ranking from Customer Reviews", International Journal of Computer and Information Engineering, vol. 9, no. 3, pp. 734-739, 2015.
  4. Zheng-Jun Zha, Jianxing Yu, Jinhui Tang, Meng Wang and Tat-Seng Chua, "Product Aspect Ranking and Its Applications", IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 5, pp. 1211-1224, 2014.
  5. A. Onan and S. Korukoglu, "A feature selection model based on genetic rank aggregation for text sentiment classification", Journal of Information Science, vol. 43, no. 1, pp. 25-38, 2016.
  6. B. Liu, "Sentiment Analysis and Opinion Mining," Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1-167, May 2012

Publication Details

Published in : Volume 5 | Issue 1 | January-February 2019
Date of Publication : 2019-01-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 45-49
Manuscript Number : CSEIT183812
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Prof. Sonali D. Borase, Prof. Prasad P. Mahale, "Aspect Ranking Technique for Efficient Opinion Mining using Sentiment Analysis : Review ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.45-49, January-February-2019. Available at doi : https://doi.org/10.32628/CSEIT183812
Journal URL : http://ijsrcseit.com/CSEIT183812

Article Preview

Follow Us

Contact Us