Recommendation System for Marketing with Sentimental Analysis Based on Customer Product Reviews Using ML Algorithms

S.M.P Qubeb; V. Yuvarajachari; B. Fayal Khan; J. Srividya; M. Sameena Begum; S. Venkatesh; G. Vamsidhar Reddy; S. Venkatesh

doi:https://doi.org/10.47001/ICJES/2025.402005

Authors

S.M.P Qubeb

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

V. Yuvarajachari

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

B. Fayal Khan

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

J. Srividya

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

M. Sameena Begum

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

S. Venkatesh

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

G. Vamsidhar Reddy

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

S. Venkatesh

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Abstract

In the modern digital era, online reviews significantly shape consumer opinions and purchasing behavior. E-commerce platforms such as Amazon and Flipkart enable users to share their experiences, offering future buyers valuable insights into product performance. To effectively analyze the vast number of reviews, it is essential to categorize them based on sentiment—positive or negative. This study focuses on applying sentiment analysis techniques to classify over 400,000 mobile phone reviews into two sentiment categories. Machine learning models, including Naïve Bayes, Support Vector Machine (SVM), and Decision Tree, were implemented for this purpose. The models' effectiveness was assessed through 10-fold cross-validation to identify the most accurate classifier.

Keywords

Data processing natural language processing (NLP) opinion mining textual data categorization artificial intelligence

Citation of this Article

S.M.P Qubeb, V. Yuvarajachari, B. Fayal Khan, J. Srividya, M. Sameena Begum, S. Venkatesh, G. Vamsidhar Reddy, & S. Venkatesh. (2025). Recommendation System for Marketing with Sentimental Analysis Based on Customer Product Reviews Using ML Algorithms. International Current Journal of Engineering and Science - ICJES, 4(2), 24-30. Article DOI: https://doi.org/10.47001/ICJES/2025.402005

References

S. Erevelles, N. Fukawa, and L. Swayne, “Big data consumer analytics and the transformation of marketing,” Journal of Business Research, vol. 69, no. 2, pp. 897–904, 2022.
P. Russom et al., “Big data analytics,” TDWI best practices report, fourth quarter, pp. 1–35, 2022.
S. Erevelles, N. Fukawa, and L. Swayne, “Big data consumer analytics and the transformation of marketing,” Journal of Business Research, vol. 69, no. 2, pp. 897–904, 2022.
V. Hatzivassiloglou and K. R. McKeown, “Predicting the se- mantic orientation of adjectives,” in Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2021, pp. 174–181.
T. Wilson, J. Wiebe, and P. Hoffmann, “Recognizing contextual polarity in phrase-level sentiment analysis,” in Proceedings of the conference on human language technology and empirical methods in natural language processing. Association for Computational Linguistics, 2021, pp. 347–354.
B. Pang and L. Lee, “A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts,” in Proceedings of the 42nd annual meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2022, p. 271.
A.Pak and P. Paroubek, “Twitter as a corpus for sentiment analysis and opinion mining.” in LREc, vol. 10, no. 2023, 2023.
M. WAHYUDI and D. A. KRISTIYANTI, “Sentiment analysis of smartphone product review using support vector machine algorithm-based particle swarm optimization.” Journal of The- oretical & Applied Information Technology, vol. 91, no. 1, 2023.
D. N. Devi, C. K. Kumar, and S. Prasad, “A feature based approach for sentiment analysis by using support vector ma- chine,” in Advanced Computing (IACC), 2023 IEEE 6th International Conference on. IEEE, 2016, pp. 3–8.
V. Narayanan, I. Arora, and A. Bhatia, “Fast and accurate sentiment classification using an enhanced naive bayes model,” in International Conference on Intelligent Data Engineering and Automated Learning. Springer, 2023, pp. 194–201.
S. Tan and J. Zhang, “An empirical study of sentiment analysis for chinese documents,” Expert Systems with applications, vol. 34, no. 4, pp. 2622–2629, 2023.
Q. Ye, Z. Zhang, and R. Law, “Sentiment classification of online reviews to travel destinations by supervised machine learning approaches,” Expert Systems with Applications, vol. 36, no. 3, pp. 6527–6535, 2023.
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,” Knowledge-Based Systems, vol. 41, pp. 89–97, 2023.
M. Hagenau, M. Liebmann, and D. Neumann, “Automated news reading: Stock price prediction based on financial news using context-capturing features,” Decision Support Systems, vol. 55, no. 3, pp. 685–697, 2023.
T. Xu, Q. Peng, and Y. Cheng, “Identifying the semantic orientation of terms using s-hal for sentiment analysis,” Knowledge- Based Systems, vol. 35, pp. 279–289, 2023.
I.Maks and P. Vossen, “A lexicon model for deep sentiment analysis and opinion mining applications,” Decision Support Systems, vol. 53, no. 4, pp. 680–688, 2023.
G. Qiu, X. He, F. Zhang, Y. Shi, J. Bu, and C. Chen, “Dasa: dissatisfaction-oriented advertising based on sentiment analysis,” Expert Systems with Applications, vol. 37, no. 9, pp. 6182– 6191, 2010.
T.-K. Fan and C.-H. Chang, “Blogger-centric contextual ad- vertising,” Expert systems with applications, vol. 38, no. 3, pp. 1777–1788, 2021.
I.Feinerer, “Introduction to the tm package text mining in r,” 2015.
M. Jockers, “Package âAŸsyuzhetâAZ,” 2022.
K. M. Leung, “Naive bayesian classifier,” Polytechnic University Department of Computer Science/Finance and Risk Engineering, 2020.
S. Tong and D. Koller, “Support vector machine active learning with applications to text classification,” Journal of machine learning research, vol. 2, no. Nov, pp. 45–66, 2021.
I.Jenhani, N. B. Amor, and Z. Elouedi, “Decision Trees as possibilistic classifiers,” International Journal of Approximate Reasoning, vol. 48, no.3, pp. 784-807, 2022.

[1] S. Erevelles, N. Fukawa, and L. Swayne, “Big data consumer analytics and the transformation of marketing,” Journal of Business Research, vol. 69, no. 2, pp. 897–904, 2022.

[2] P. Russom et al., “Big data analytics,” TDWI best practices report, fourth quarter, pp. 1–35, 2022.

[3] S. Erevelles, N. Fukawa, and L. Swayne, “Big data consumer analytics and the transformation of marketing,” Journal of Business Research, vol. 69, no. 2, pp. 897–904, 2022.

[4] V. Hatzivassiloglou and K. R. McKeown, “Predicting the se- mantic orientation of adjectives,” in Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2021, pp. 174–181.

[5] T. Wilson, J. Wiebe, and P. Hoffmann, “Recognizing contextual polarity in phrase-level sentiment analysis,” in Proceedings of the conference on human language technology and empirical methods in natural language processing. Association for Computational Linguistics, 2021, pp. 347–354.

[6] B. Pang and L. Lee, “A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts,” in Proceedings of the 42nd annual meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2022, p. 271.

[7] A.Pak and P. Paroubek, “Twitter as a corpus for sentiment analysis and opinion mining.” in LREc, vol. 10, no. 2023, 2023.

[8] M. WAHYUDI and D. A. KRISTIYANTI, “Sentiment analysis of smartphone product review using support vector machine algorithm-based particle swarm optimization.” Journal of The- oretical & Applied Information Technology, vol. 91, no. 1, 2023.

[9] D. N. Devi, C. K. Kumar, and S. Prasad, “A feature based approach for sentiment analysis by using support vector ma- chine,” in Advanced Computing (IACC), 2023 IEEE 6th International Conference on. IEEE, 2016, pp. 3–8.

[10] V. Narayanan, I. Arora, and A. Bhatia, “Fast and accurate sentiment classification using an enhanced naive bayes model,” in International Conference on Intelligent Data Engineering and Automated Learning. Springer, 2023, pp. 194–201.

[11] S. Tan and J. Zhang, “An empirical study of sentiment analysis for chinese documents,” Expert Systems with applications, vol. 34, no. 4, pp. 2622–2629, 2023.

[12] Q. Ye, Z. Zhang, and R. Law, “Sentiment classification of online reviews to travel destinations by supervised machine learning approaches,” Expert Systems with Applications, vol. 36, no. 3, pp. 6527–6535, 2023.

[13] 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,” Knowledge-Based Systems, vol. 41, pp. 89–97, 2023.

[14] M. Hagenau, M. Liebmann, and D. Neumann, “Automated news reading: Stock price prediction based on financial news using context-capturing features,” Decision Support Systems, vol. 55, no. 3, pp. 685–697, 2023.

[15] T. Xu, Q. Peng, and Y. Cheng, “Identifying the semantic orientation of terms using s-hal for sentiment analysis,” Knowledge- Based Systems, vol. 35, pp. 279–289, 2023.

[16] I.Maks and P. Vossen, “A lexicon model for deep sentiment analysis and opinion mining applications,” Decision Support Systems, vol. 53, no. 4, pp. 680–688, 2023.

[17] G. Qiu, X. He, F. Zhang, Y. Shi, J. Bu, and C. Chen, “Dasa: dissatisfaction-oriented advertising based on sentiment analysis,” Expert Systems with Applications, vol. 37, no. 9, pp. 6182– 6191, 2010.

[18] T.-K. Fan and C.-H. Chang, “Blogger-centric contextual ad- vertising,” Expert systems with applications, vol. 38, no. 3, pp. 1777–1788, 2021.

[19] I.Feinerer, “Introduction to the tm package text mining in r,” 2015.

[20] M. Jockers, “Package âAŸsyuzhetâAZ,” 2022.

[21] K. M. Leung, “Naive bayesian classifier,” Polytechnic University Department of Computer Science/Finance and Risk Engineering, 2020.

[22] S. Tong and D. Koller, “Support vector machine active learning with applications to text classification,” Journal of machine learning research, vol. 2, no. Nov, pp. 45–66, 2021.

[23] I.Jenhani, N. B. Amor, and Z. Elouedi, “Decision Trees as possibilistic classifiers,” International Journal of Approximate Reasoning, vol. 48, no.3, pp. 784-807, 2022.