Implementation of User Rating Classification for Amazon Food Review Dataset Using SVM and LSTM


  • Krishan Kumar, Randeep Singh


SVM, LSTM, User rating classification, sentiment analysis


This research investigates the challenge of classifying user ratings for Amazon food reviews using Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) neural networks. The objective is to forecast the sentiment or user rating categorization of food reviews in order to provide important information for both consumers and vendors on the network. The dataset comprises textual reviews and their related user ratings collected from the Amazon food goods category. A train-test split is conducted in order to train the Support Vector Machine (SVM) model using the training dataset and adjust its hyperparameters to achieve optimal performance. In the context of Long Short-Term Memory (LSTM), the neural network is trained by using the training set and incorporating strategies such as dropout and early stopping to mitigate the issue of overfitting. The empirical findings demonstrate that both Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) models exhibit a notable level of precision when used for the purpose of forecasting user ratings in the context of Amazon food reviews. Support Vector Machines (SVM) have exceptional performance in managing datasets that are both sparse and high-dimensional. On the other hand, Long Short-Term Memory (LSTM) networks are very proficient at capturing contextual connections within textual data. The results provide significant insights for organisations about customer satisfaction and sentiment patterns, enabling them to make informed choices based on data to enhance product offerings and improve customer experiences. In addition, prospective consumers might get advantages from the precise sentiment analysis while evaluating food acquisitions on the Amazon platform.


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B. Liu, Sentiment Analysis And Opinion Mining. San Rafael, Ca, Usa: Morgan & Claypool, May 2012.

L.-C. Yu, J.-L. Wu, P.-C. Chang, And H.-S. Chu, “Using A Contextual Entropy Model To Expand Emotion Words And Their Intensity For The Sentiment Classification Of Stock Market News,” Knowl.- Based Syst., Vol. 41, Pp. 89–97, Mar. 2013. [Online]. Available: Http://Www.Sciencedirect.Com/Science/Article/Pii/S095070511300004x

M. Hagenau, M. Liebmann, And D. Neumann, “Automated News Reading: Stock Price Prediction Based On Financial News Using Context-Capturing Features,” Decis. Support Syst., Vol. 55, No. 3, Pp. 685–697, Jun. 2013. [Online]. Available: Http://Www.Sciencedirect.Com/Science/Article/Pii/S0167923613000651

T. Xu, Q. Peng, And Y. Cheng, “Identifying The Semantic Orientation Of Terms Using S-Hal For Sentiment Analysis,” Knowl.-Based Syst., Vol. 35, Pp. 279–289, Nov. 2012. [Online]. Available: Https:// Www.Sciencedirect.Com/ Science/Article/ Abs/Pii/S0950705112001074

Valdivia, M. V. Luzón, And F. Herrera, “Sentiment Analysis In Tripadvisor,” Ieee Intell. Syst., Vol. 32, No. 4, Pp. 72–77, Aug. 2017.

W. Medhat, A. Hassan, And H. Korashy, “Sentiment Analysis Algorithms And Applications: A Survey,” Ain Shams Eng. J., Vol. 5, No. 4, Pp. 1093–1113, Dec. 2014. [Online]. Available:

Http://Www.Sciencedirect.Com/ Science/Article/ Pii/ S2090447914000550

Schoenmueller, V., Netzer, O. And Stahl, F. 2020. The Polarity Of Online Reviews: Prevalence, Drivers And Implications. Journal Of Marketing Research (Jmr), 57(5), Pp. 853–877.

Karamitsos, I., Albarhami, S. And Apostolopoulos, C., 2019. Tweet Sentiment Analysis (Tsa) For Cloud Providers Using Classification Algorithms And Latent Semantic Analysis. Journal Of Data Analysis And Information Processing, 7(4), Pp.276-294.

Zhao, Y., 2013. R And Data Mining: Examples And Case Studies. Academic Press.

Jain, V.K., Kumar, S. And Mahanti, P., 2018. Sentiment Recognition In Customer Reviews Using Deep Learning. International Journal Of Enterprise Information Systems (Ijeis), 14(2), Pp.77-86.

Lim, J., Park, M., Anitsal, S., Anitsal, M.M. And Anitsal, I. 2019. 'Retail Customer Sentiment Analysis: Customers' Reviews Of Top Ten Us Retailers' Performance,' Global Journal Of Management And Marketing, 3(1), 124+.

Jagdale, R.S., Shirsat, V.S. And Deshmukh, S.N., 2019. Sentiment Analysis On Product Reviews Using Machine Learning Techniques. In Cognitive Informatics And Soft Computing (Pp. 639-647). Springer, Singapore.

Sharma, S. K., Chakraborti, S. And Jha, T. 2019. Analysis Of Book Sales Prediction At Amazon Marketplace In India: A Machine Learning Approach. Information Systems And E-Business Management, 17(2–4), Pp. 261–284.

Chong, A.Y.L., Li, B., Ngai, E.W., Chang, E. And Lee, F., 2016. Predicting Online Product Sales Via Online Reviews, Sentiments, And Promotion Strategies: A Big Data Architecture And Neural Network Approach. International Journal Of Operations & Production Management.

Du, J, Rong, J, Michalska, S, Wang, H & Zhang, Y. 2019. Feature Selection For Helpfulness Prediction Of Online Product Reviews: An Empirical Study', Plos One, 14(12), P. E0226902.

Meenakshi, A.B., Intwala, N., And Sawant, V., 2020. Sentiment Analysis Of Amazon Mobile Reviews. Ict Systems And Sustainability: Proceedings Of Ict4sd 2019, Volume 1, 1077, P.43.

Govindaraj, S. And Gopalakrishnan, K. 2016. Intensified Sentiment Analysis Of Customer Product Reviews Using Acoustic And Textual Features. Etri Journal, 38(3), Pp. 494–501.

Ghasemaghaei, M., Eslami, Sp, Deal, K. And Hassanein, K., 2018. Reviews' Length And Sentiment As Correlates Of Online Reviews' Ratings. Internet Research.

Singla, Z., Randhawa, S. And Jain, S., 2017. Statistical And Sentiment Analysis Of Consumer Product Reviews. In 2017 8th International Conference On Computing, Communication And Networking Technologies (Icccnt) (Pp. 1-6). Ieee.

R. Xia, C. Zong, And S. Li, “Ensemble Of Feature Sets And Classification Algorithms For Sentiment Classification,” Information Sciences, Vol. 181,No. 6, Pp. 1138–1152, 2011.

G. Gautam And D. Yadav, “Sentiment Analysis Of Twitter Data Usingmachine Learning Approaches And Semantic Analysis,” In 2014 Seventh International Conference On Contemporary Computing (Ic3). Ieee, 2014, Pp. 437–442.

X. Jiang, "A Facial Expression Recognition Model Based On Hmm," Proceedings Of 2011 International Conference On Electronic & Mechanical Engineering And Information Technology, Harbin, Heilongjiang, China, 2011.

W. Swinkels, L. Claesen, F. Xiao And H. Shen, "Svm Point-Based Real-Time Emotion Detection," 2017 Ieee Conference On Dependable And Secure Computing, Taipei, 2017.

Sundermeyer, Martin, Ralf Schlter, And Hermann Ney. ”Lstm Neural Networks For Language Modeling.” Interspeech. 2012.

Zhou, Zhenxiang. “Amazon Food Review Classification Using Deep Learning And Recommender System.” (2016).

G. Sathish S. V. Saravanan’s. NarmadhaS. U. Maheswari 2012.

“Personal Authentication System Using Hand Vein Biometric.”

International Journal Of Computertechnology And Applications Vol. 3(1)

Pp. 383-391 Jan-Feb 2012.




How to Cite

Krishan Kumar. (2024). Implementation of User Rating Classification for Amazon Food Review Dataset Using SVM and LSTM. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3063–3072. Retrieved from



Research Article