An Approach for Product Recommendation using Light GBM

Authors

  • I. S. Siva Rao Associate Professor, Department of CSE, GITAM University, Rushikonda, Visakhapatnam.
  • Parasa Rajya Lakshmi Assistant professor, Department of Information Technology, Prasad V. Potluri Siddhartha Institute Of Technology, Vijayawada, AP.
  • Dasari N. V. Syma Kumar Associate Professor, Department of CSE, Bapatla Engineering College, Bapatla, AP, India.
  • Akkala Yugandhara Reddy Assistant professor, Department of Computer Science and Engineering, Chirala Engineering College, Chirala, A.P, India.
  • Jayavarapu Karthik Associate Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India.
  • Badugu Bhavana Department of CSE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India.

Keywords:

ADABoost, Extreme Gradient Boosting, Gradient Boosting, Light GBM, Random Forest, Recommendations

Abstract

Attracting clients is the main task of online e-commerce websites. Systems for providing recommendations are essential for engaging clients. Customer reviews play a crucial role in analyzing the product. Product insights can be provided by sentiment analysis of customer reviews. Websites routinely recommend products despite bad user reviews, which dissatisfy customers. Hence there is a need for a more accurate model recommending the products. In this work, a machine learning model is proposed that suggests a product with a greater user sentiment for positivity. Models are developed to analyze the sentiment of product reviews using the algorithms ADABoost, Light GBM, Gradient Boosting, Extreme Gradient Booting, and Extreme Gradient Boosting coupled with Random Forest. Based on the performance of the models, the Light GBM model is considered for building the product recommendation system.  The proposed model gave better results when compared to existing models.

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References

P. Sasikala, L. Mary Immaculate Sheela, "Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS", Journal of Big Data, pp. 1-20.

Xing Fang, Justin Zhan, "Sentiment analysis using product review data", Journal of Big Data.

In Lee, George Mangalaraj, "Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions", Big data and cognitive computing.

DANA A. AL-QUDAH, ALA M. AL-ZOUBI, PEDRO A. CASTILLO-VALDIVIESO, HOSSAM FARIS, "Sentiment Analysis for e-Payment Service Providers Using EvolutionaryeXtreme Gradient Boosting", IEEE Access volume 8, 2020.

Praphula Kumar Jain, Rajendra Pamula, Gautam Srivastava, "A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews", Computer Science Review 41 (2021) 100413.

Anindya Ghose, Panagiotis G. Ipeirotis, "Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics", IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol 23, no 10.

Robert Ireland, Ang Liu, "Application of data analytics for product design: Sentiment analysis of online product reviews", CIRP Journal of Manufacturing Science and Technology 23 (2018) pp. 128-144.

Reinald Kim Amplayo, Seanie Lee, Min Song, "Incorporating product description to sentiment topic models for improved aspect-based sentiment analysis", Information Sciences 454-455 (2018) pp. 200-215.

Xiaolin Lia, Chaojiang Wub, Feng Maic, "The effect of online reviews on product sales: A joint sentiment-topic analysis", Information & Management 56 (2019) pp. 172-184.

Yang Liu, Jian-Wu Bi, Zhi-Ping Fan, "Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory", Information Fusion 36 (2017) pp. 149-161.

Jayakumar Sadhasivam, Ramesh Babu Kalivaradhan, "Sentiment Analysis of Amazon Products Using Ensemble Machine Learning Algorithm", International Journal of Mathematical, Engineering and Management Sciences, Vol. 4, No. 2, 508–520, 2019.

Anu Dahiya, Nidhi Gautam, Prashant Kumar Gautam, "Data Mining Methods and Techniques for Online Customer Review Analysis: A Literature Review", Journal of System and Management Sciences, Vol. 11 (2021) No. 3, pp. 1-26.

Hassan Raza, M. Faizan, Ahsan Hamza, Ahmed Mushtaq, Naeem Akhtar, "Scientific Text Sentiment Analysis using Machine Learning Techniques", (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 12, 2019.

Qurat Tul Ain, Mubashir Ali, Amna Riazy, Amna Noureenz, Muhammad Kamranz, Babar Hayat, A. Rehman, "Sentiment Analysis Using Deep Learning Techniques: A Review", International Journal of Advanced Computer Science and Applications, Vol. 8, No. 6, 2017.

Supriya B, C.B. Akki, "Sentiment Prediction using Enhanced XGBoost and Tailored Random Forest", International Journal of Computing and Digital Systems, ISSN (2210-142X).

Maganti Syamala1, Nattanmai Jeganathan Nalini, "A Filter Based Improved Decision Tree Sentiment Classification Model for Real-Time Amazon Product Review Data", International Journal of Intelligent Engineering and Systems.

Li Chen, Dongning Yan, Feng Wang "User perception of sentiment-integrated critiquing in recommender systems", International Journal of Human-Computer Studies.

Efstratios Kontopoulos, Christos Berberidis, Theologos Dergiades, Nick Bassiliades "Ontology-based sentiment analysis of Twitter posts", Expert Systems with Applications.

A.Nisha Jebaseeli, E.Kirubakaran, “A Survey on Sentiment Analysis of(product) Reviews”, International Journal of Computer Applications(0975-888), volume 47- no.11

Gayatri Khanvilkar, Deepali Vora, “ Product Recommendation using Sentiment Analysis of reviews: A Random Forest Approach”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249-8958, Volume-8, Issue-2S2.

Dr. D. Sivaganesan, Sridhar K, Dhruv Aggarwal, Arunkumar M, “ Feature based Sentiment Analysis for Product Reviews”, International Journal of Engineering Research & Technology, ISSN: 2278-0181, Volume 11, Issue 06.

Adinarayana Salina, K Yogeswara Rao Yogi Rao, G.S.N Murthy, “Product Recommendation System from Users Reviews using Sentiment Analysis”, International Journal of Computer Applications, Volume-169.

Najma Sultana, Pintu kumar, Sourabh Chandra, SK Safikul Alam, “Sentiment Analysis for Product Review”, International Journal of Soft Computing, pg:1913-1919.

Dishi Jain, Bitra Harsha Vardhan, Sravanakumar Kandasamy, “Sentiment Analysis Of Product Review- A Survey”, International Journal of Scientific & Technology Research, Volume 8, Issue 12.

Raktim Kumar Dey, Debabrata Sarddhar, Rajesh Bose, Sandip Roy, “ A Literature Survey on Sentiment Analysis Techniques involving Social Media and Online Platforms”, International Journal of Scientific & Technology Research, Vol 1 Issue 1.

Rajkumar S.Jagdale, Vishl S. Shirsat, Sachin N.Deshmukh, “Sentiment Analysis on Product Reviews Using Machine Learning Techniques”, Cognitive Informatics and Soft Computing, Adavnces in Intelligent Systems and Computing 768, pg:639-647

B. Bhavana, Jayavarapu Karthik, P. Lakshmi Kumari. “A Novel Approach for Product Recommendation using XGBOOST”, 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things(IDCIoT), 2023.

Yadav, Vijay, and Subarna Shakya. "Sentiment Analysis and Topic Modeling on News Headlines. "Journal of Ubiquitous Computing and Communication Technologies 4, no. 3 (2022): 204-218.

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Published

23.02.2024

How to Cite

Rao, I. S. S. ., Lakshmi, P. R. ., Kumar, D. N. V. S. ., Reddy, A. Y. ., Karthik, J. ., & Bhavana, B. . (2024). An Approach for Product Recommendation using Light GBM. International Journal of Intelligent Systems and Applications in Engineering, 12(17s), 561–570. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4921

Issue

Vol. 12 No. 17s (2024)

Section

Research Article

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