Journal of Information

Published by: Conscientia Beam
Online ISSN: 2520-7652
Print ISSN: Pending
Quick Submission    Login/Submit/Track

No. 1

Big Data Frameworks for Sites and Products Recommendation

Pages: 1-14
Find References

Finding References

Big Data Frameworks for Sites and Products Recommendation

Search :
Google Scholor
Search :
Microsoft Academic Search

DOI: 10.18488/journal.104.2021.61.1.14

Ogbuju, E. , Ejiofor, V. , Okonkwo, O. , Onyesolu, M.

Export to    BibTeX   |   EndNote   |   RIS

Alenezi, S., & Mesbah, S. (2015). Big data spatial analytics in social networks using Hadoop. International Journal of Computer Applications, 128(14), 21-26. Available at:

Batool, R., Khattak, A. M., Maqbool, J., & Sungyoung, L. (2013). Precise tweet classification and sentiment analysis. Paper presented at the IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS).

Cheng, O., & Lau, R. (2015). Big Data stream analytics for near real-time sentiment analysis. Journal of Computer and Communications, 3(1), 189-195. Available at: 10.4236/jcc.2015.35024.

Denecke, K. (2008). Using sentiwordnet for multilingual sentiment analysis. Paper presented at the In 2008 IEEE 24th International Conference on Data Engineering Workshop, IEEE.

Esfandiari, K., Honarvar, A., & Aghamirzadeh, S. (2016). Improvement of recommender systems considering big data of users’ comments on chosen items. Journal of Fundamental and Applied Sciences, 8(2), 882-891. Available at:

Flesch, B. (2014). Design, development and evaluation of a big data analytics dashboard, [M.Sc Thesis]. Retrieved from: .

Gupta, P., Kumar, P., & Gopal, G. (2015). Sentiment analysis on Hadoop with Hadoop streaming. International Journal of Computer Applications, 121(11), 0975 – 8887.

Howe, M. (2009). Pandora’s music recommender. Retrieved from:

Kabiljo, M., & Ilic, A. (2015). Recommending items to more than a billion people. Retrieved from:

Kim, D., Park, C., Oh, J., & Yu, H. (2017). Deep hybrid recommender systems via exploiting document context and statistics of items. Information Sciences, 417, 72-87. Available at:

Kismet, K. (2017). Netflix: Recommendations worth a million. Retrieved from:

Kumar, S. P., Sachdeva, A., Mahajan, D., Pande, N., & Sharma, A. (2014). An approach towards feature specific opinion mining and sentimental analysis across e-commerce websites. Paper presented at the 5th International Conference of the Next Generation Information Technology Summit (Confluence).

Larose, D. T., & Larose, C. (2015). Data mining and predictive analytics (2nd ed.). New Jersey: John Wiley & Sons, Inc., Hokoken.

Linden, G., Smith, B., & York, J. (2003). recommendations item-to-item collaborative filtering. Paper presented at the IEEE Computer Society.

Liu, B. (2015). Sentiment analysis: Mining opinions, sentiments, and Emotions. UK: Cambridge University Press.

Luo, Y., Miao, F., & Xiaoxia, Z. (2011). The design and implementation of feature-grading recommendation system for e-commerce. Paper presented at the IEEE International Conference on Information and Automation (ICIA).

Mali, D., Abhyankar, M., Bhavarthi, P., Gaidhar, K., & Bangare, M. (2016). Sentiment analysis of product reviews for E-commerce recommendation. International Journal of Management and Applied Science, 2(1), 127-130.

Murugavalli, S., Bagirathan, U., Saiprassanth, R., & Arvindkumar, S. (2017). Feedback analysis using sentiment analysis for e-commerce. International Journal of Latest Engineering Research and Applications (IJLERA), 2(3), 89 – 90.

Nair, A. S., & Sreelakshmi, K. (2017). Movie recommendation system using sentiment analysis. International Journal For Trends In Engineering & Technology, 24(1), 28-30.

Ogbuju, E., Ejiofor, V., Ihinkalu, O., & Ajulo, E. B. (2017). Sentiment analysis for rules-driven instant messaging. Confluence Journal of Pure and Applied Sciences (CJPAS), 1(1), 241-155.

Piatetsky, G. (2014). CRISPDM: still the top methodology for analytics, data mining, or data science projects [Web log post]. Retrieved from: .

Priyadharsini, R. L., & Felciah, M. L. (2017). Recommendation system in e-commerce using sentiment analysis. International Journal of Engineering Trends and Technology (IJETT), 49(7), 445-450.

Ray, P., & Chakrabarti, A. (2017). Twitter sentiment analysis for product review using lexicon method. Paper presented at the Paper presented at the International Conference on Data Management, Analytics and Innovation (ICDMAI) 2017. IEEE.

Salvi, K., Pawar, V., Kadu, P., & Dike, O. D. (2016). Shopping site recommendation using sentiment analysis. International Journal of Innovative Research in Computer and Communication Engineering, 4(4), 6990-6993. Available at: 10.15680/IJIRCCE.2016.0404103.

Singh, V. K., Piryani, R., Uddin, A. P., & Marisha, W. (2013). Sentiment analysis of  textual reviews, evaluating Machine Learning, unsupervised and sentiwordnet approaches. Paper presented at the Proceeding in IEEE 2013 5th International Conference on Knowledge and Smart Technology (KST).

Song, H., Fan, Y., Liu, X., & Tao, D. (2011). Extracting product features from online reviews for sentimental analysis. Paper presented at the Proceedings of the 6th International Conference on Computer Sciences and Convergence Information Technology (ICCIT).

Taylor, J. (2016). Decision modeling and notation standard [PowerPoint slides]. Retrieved from: .

Wang, J., & Tang, Q. (2015). Recommender systems and their security concerns.Retrieved from:

No any video found for this article.
Ogbuju, E. , Ejiofor, V. , Okonkwo, O. , Onyesolu, M. (2021). Big Data Frameworks for Sites and Products Recommendation. Journal of Information, 6(1): 1-14. DOI: 10.18488/journal.104.2021.61.1.14
The improvement of the IT infrastructure in an e-commerce platform is essential for both customer satisfaction and increased revenue. While different techniques had been applied towards achieving this, there is still need to engage customer feedbacks in providing an all-inclusive solution to the recommendation systems available in the e-commerce domain. The motivation is on making a more exact recommendation with the traditional collaborative system by mining the feedbacks and uncovering their sentiments using big data analytic systems. This paper describes the design of a big data framework that may be used for shopping sites recommendations and another that may be used for product(s) recommendations to prospecting customers. The use of the cross industry standard process for data mining is applied in proposing the new system. Although the techniques of Hadoop/MongoDB tools are described within the proposed designs, it concentrates mainly on the architecture and algorithm of the system in a holistic approach to enable the platform providers, e-commerce merchants and practitioners find a guided implementation of it using any tool of choice.
Contribution/ Originality
This study documents the use of the cross-industry standard process for data mining methodology to design big data frameworks that implement the application of sentiment analysis of review texts as preprocessed input into the collaborative filtering algorithm for both sites and product recommendation.