Open Access   Article Go Back

Recommendation Systems, Incorporating Sentiment Analysis with Specific Reference to the Academic Domain

Anil Kumar1 , Sonal Chawla2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-9 , Page no. 17-22, Sep-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i9.1722

Online published on Sep 30, 2018

Copyright © Anil Kumar, Sonal Chawla . 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: Anil Kumar, Sonal Chawla, “Recommendation Systems, Incorporating Sentiment Analysis with Specific Reference to the Academic Domain,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.17-22, 2018.

MLA Style Citation: Anil Kumar, Sonal Chawla "Recommendation Systems, Incorporating Sentiment Analysis with Specific Reference to the Academic Domain." International Journal of Computer Sciences and Engineering 6.9 (2018): 17-22.

APA Style Citation: Anil Kumar, Sonal Chawla, (2018). Recommendation Systems, Incorporating Sentiment Analysis with Specific Reference to the Academic Domain. International Journal of Computer Sciences and Engineering, 6(9), 17-22.

BibTex Style Citation:
@article{Kumar_2018,
author = {Anil Kumar, Sonal Chawla},
title = {Recommendation Systems, Incorporating Sentiment Analysis with Specific Reference to the Academic Domain},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2018},
volume = {6},
Issue = {9},
month = {9},
year = {2018},
issn = {2347-2693},
pages = {17-22},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2815},
doi = {https://doi.org/10.26438/ijcse/v6i9.1722}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i9.1722}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2815
TI - Recommendation Systems, Incorporating Sentiment Analysis with Specific Reference to the Academic Domain
T2 - International Journal of Computer Sciences and Engineering
AU - Anil Kumar, Sonal Chawla
PY - 2018
DA - 2018/09/30
PB - IJCSE, Indore, INDIA
SP - 17-22
IS - 9
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
865 640 downloads 330 downloads
  
  
           

Abstract

In recent years, Internet is growing exponentially and so is the amount of learning resources. Due to overload of information, learners find it difficult to retrieve the appropriate learning resource. In academic domain, recommendation systems are facing problems in providing accurate suggestions to learner due to difference in types of learning resources, learner preferences, knowledge level and quality of the learning resource. In this context, the objective of this paper is four folds: Firstly, the paper discusses various techniques used in creation of recommendation system with a special focus on Academic Domain. Secondly, it compares and contrasts the existing recommender systems in practice today. Thirdly, the paper looks at the possibility of including Sentiment Analysis as an effective technique for recommending learning resources to the learners & it goes at length to give a sequential flow chart for a pilot study of book recommender system. Finally, the paper concludes by drawing the inferences on the introduction of sentiment analysis as a useful technique for recommendation system.

Key-Words / Index Term

Book Recommendation System, Recommendation System, Sentiment Analysis

References

[1] M. N. Moreno, S. Segrera, Vivian F. López, M. D. Muñoz ,Á. L. Sánchez “Web mining based framework for solving usual problems in recommender systems. A case study for movies` recommendation,” Neurocomputing, Vol. 176, pp. 72–80, 2015.
[2] J. Bobadilla F. Ortega, A. Hernando, A. Gutiérrez, “Recommender systems survey,” Knowledge-Based System, Vol. 46, pp. 109–132, 2013.
[3] J. Lu, D. Wu, M. Mao, W. Wang, G. Zhang, “Recommender system application developments: A survey,” Decision Support System, Vol. 74, pp. 12–32, 2015.
[4] A. Klašnja-Milićevic, M. Ivanovi, A. Nanopoulos, “Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions,” Artificial Intelligence Review, Vol. 44, no. 4, pp. 571–604, 2015.
[5] S. E. Middleton, N. R. Shadbolt, D. C. De Roure, “Ontological User Profiling in Recommender Systems,” ACM Transactions on Information Systems, Vol. 22, No. 1, pp. 54–88, 2004.
[6] K. I. Bin Ghauth, N. A. Abdullah, “Building an e-learning recommender system using Vector Space Model and good learners average rating,” in Proceedings of 9th IEEE International Conference on Advanced Learning Technologies, pp. 194–196, 2016.
[7] F. Abel, I. I. Bittencourt, E. Costa, N. Henze, D. Krause, J. Vassileva, “Recommendations in online discussion forums for e-learning systems,” IEEE Transactions On Learning Technologies, Vol. 3, no. 2, pp. 165–176, 2010.
[8] A. Klašnja-Milićević, B. Vesin, M. Ivanovi, Z. Budimac, “E-Learning personalization based on hybrid recommendation strategy and learning style identification,” Computers & Education, Vol. 56, no. 3, pp. 885–899, 2011.
[9] C. Rana, S. K. Jain, “Building a book recommender system using time based content filtering,” WSEAS Transactions on Computers, Vol. 11, no. 2, pp. 27–33, 2012.
[10] B. Vesin, A. Klašnja-Milićević, “APPLYING RECOMMENDER SYSTEMS AND ADAPTIVE HYPERMEDIA FOR E-LEARNING PERSONALIZATION,” Computing and Informatics, Vol. 32, pp. 629–659, 2013.
[11] S. Shishehchi, S. Y. Banihashem, N. A. M. Zin, S. A. M. Noah, “Ontological approach in knowledge based recommender system to develop the quality of e-learning system,” Australian Journal of Basic and Applied Sciences, Vol. 6, no. 2, pp. 115–123, 2012.
[12] S. B. Aher, L. M. R. J. Lobo, “Knowledge-Based Systems Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data,” Knowledge-Based Systems, Vol. 51, pp. 1–14, 2013.
[13] W. Chen, Z. Niu,X. Zhao, Y. Li, “A hybrid recommendation algorithm adapted in e-learning environments,” World Wide Web, Vol. 17, no. 2, pp. 271–284, 2014.
[14] S. Fraihat, Q. Shambour, “A Framework of Semantic Recommender System for e- Learning,” Journal of Software, Vol. 10, no. 3, pp. 317–330, 2015.
[15] J. Bernab, E. Herrera-viedma, “A dynamic recommender system as reinforcement for personalized education by a fuzzly linguistic web system,” Procedia Computer Science, Vol. 55, no. Itqm, pp. 1143–1150, 2015.
[16] Y. Zhang, “Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized Recommendation,” in Proceeding of WSDM `15, Shanghai, China, February 02 - 06, pp. 435–439, 2015
[17] J. K. Tarus, Z. Niu, A. Yousif, “A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining,” Future Generation Computer System, Vol. 72, pp. 37–48, 2017.
[18] A. Koukourikos, G. Stoitsis, P. Karampiperis , “Sentiment Analysis : A tool for Rating Attribution to Content in Recommender Systems,” in Proceedings of RecSysTEL 2012, pp. 61–70, 2012.
[19] R. Sikka, A. Dhankar, C. Rana, “A Survey Paper on E-Learning Recommender System,” International Journal of Computer Applications, Vol. 47, no. 9, pp. 27–30, 2012.
[20] J. Serrano-Guerrero, J. A. Olivas, F. P. Romeroa, E. Herrera-Viedma , “Sentiment analysis: A review and comparative analysis of web services," Information Sciences, Vol. 311, pp. 18–38, 2015.