(College of Computer Science and Information Technology, Kirkuk University, Iraq)(2) Selim Buyrukoglu
(Computer Engineering, Faculty of Engineering, Cankiri Karatekin University, Turkey)(3) Mohammed Rashad Baker
(Software department, College of Computer Science and Information Technology. Kirkuk University, Iraq)*corresponding author
AbstractOnline platforms enhance customer engagement and provide businesses with valuable data for predictive analysis, critical for strategic sales forecasting and customer relationship management. This study explores in depth the potential of sentiment analysis (SA) to enhance sales forecasting and customer retention for small and large businesses. We collected a large dataset of product review tweets, representing a rich consumer sentiment source. We developed an artificial neural network based on a dataset of product review tweets that captures both positive and negative sentiments. The core of our model is Bi-LSTM (Bidirectional Long Short-Term Memory) architecture, enhanced by an attention mechanism to capture relationships between words and emphasize key terms. Then, a one-dimensional convolutional neural network with 64 filters of size 3x3 is applied, followed by Average_Max_Pooling to reduce the feature map. Finally, two dense layers classify the sentiment as positive or negative. This research provides significant benefits and contributions to sentiment analysis by accurately identifying consumer sentiment in product review tweets. The proposed model that integrated Bi-LSTM with attention mechanism and CNN detects negative sentiment with a precision of 0.97, recall of 0.98, and F1-score of 0.98, allowing companies to address customer concerns, improving satisfaction and brand loyalty proactively. In addition, the proposed model presents a better sentiment classification on average for both positive and negative sentiments, and accuracy (96%) compared to the other baselines. It ensures high-quality input data by reducing noise and inconsistencies in product review tweets. Moreover, the dataset collected in this study serves as a valuable benchmark for future research in sentiment analysis and predictive analytics.
KeywordsSentiment Analysis (SA); Deep Learning ;Customer Engagement; Product Reviews; Bi-LSTM Architecture
|
DOIhttps://doi.org/10.26555/ijain.v11i1.1848 |
Article metricsAbstract views : 1045 | PDF views : 226 |
Cite |
Full Text Download
|
References
[1] T. Bhumika, Jyoti, G. Neha, and K. Santosh, “Overview of Electronic commerce (E-commerce),” i-manager’s J. Inf. Technol., vol. 11, no. 2, p. 29, 2022, doi: 10.26634/jit.11.2.18955.
[2] S. Baehre, “From Research to Action: Enhancing Net Promoter Score Utilization in Managerial Practice,” Int. J. Mark. Res., vol. 66, no. 2–3, pp. 174–181, May 2024, doi: 10.1177/14707853231209893.
[3] D. M. Goldberg and A. S. Abrahams, “Sourcing product innovation intelligence from online reviews,” Decis. Support Syst., vol. 157, p. 113751, 2022, doi: 10.1016/j.dss.2022.113751.
[4] C. Burnay, M. Lega, and S. Bouraga, “Business intelligence and cognitive loads: Proposition of a dashboard adoption model,” Data Knowl. Eng., vol. 152, p. 102310, Jul. 2024, doi: 10.1016/j.datak.2024.102310.
[5] M. Nowak and M. Paw?owska-Nowak, “Dynamic Pricing Method in the E-Commerce Industry Using Machine Learning,” Appl. Sci., vol. 14, no. 24, p. 11668, Dec. 2024, doi: 10.3390/app142411668.
[6] M. R. Baker, E. Z. Mohammed, and K. H. Jihad, “Prediction of Colon Cancer Related Tweets Using Deep Learning Models,” in Lecture Notes in Networks and Systems, 2023, vol. 646 LNNS, pp. 522–532, doi: 10.1007/978-3-031-27440-4_50.
[7] M. R. Baker, Z. N. Mahmood, and E. H. Shaker, “Ensemble Learning with Supervised Machine Learning Models to Predict Credit Card Fraud Transactions,” Rev. d’Intelligence Artif., vol. 36, no. 4, pp. 509–518, 2022, doi: 10.18280/ria.360401.
[8] Y. Song and B. Yoon, “Prediction of infectious diseases using sentiment analysis on social media data,” PLoS One, vol. 19, no. 9, p. e0309842, Sep. 2024, doi: 10.1371/journal.pone.0309842.
[9] N. L. Rane, S. P. Choudhary, and J. Rane, “Artificial Intelligence, Natural Language Processing, and Machine Learning to Enhance e-Service Quality on e-Commerce Platforms,” Int. J. Artif. Intell. Mach. Learn., vol. 4, no. 2, pp. 67–82, Jul. 2024, doi: 10.51483/IJAIML.4.2.2024.67-82.
[10] M. Rodríguez-Ibánez, A. Casánez-Ventura, F. Castejón-Mateos, and P.-M. Cuenca-Jiménez, “A review on sentiment analysis from social media platforms,” Expert Syst. Appl., vol. 223, p. 119862, Aug. 2023, doi: 10.1016/j.eswa.2023.119862.
[11] M. S. Md Suhaimin, M. H. Ahmad Hijazi, E. G. Moung, P. N. E. Nohuddin, S. Chua, and F. Coenen, “Social media sentiment analysis and opinion mining in public security: Taxonomy, trend analysis, issues and future directions,” J. King Saud Univ. - Comput. Inf. Sci., vol. 35, p. 101776, no. 9, 2023, doi: 10.1016/j.jksuci.2023.101776.
[12] F. Greco, “Sentiment analysis and opinion mining,” Elgar Encycl. Technol. Polit., vol. 5, no. 1, pp. 105–108, 2022, doi: 10.4337/9781800374263.sentiment.analysis.
[13] M. S. Islam et al., “‘Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach,’” Artif. Intell. Rev., vol. 57, no. 3, p. 62, Mar. 2024, doi: 10.1007/s10462-023-10651-9.
[14] Y. C. Hua, P. Denny, J. Wicker, and K. Taskova, “A systematic review of aspect-based sentiment analysis: domains, methods, and trends,” Artif. Intell. Rev., vol. 57, no. 11, p. 296, Sep. 2024, doi: 10.1007/s10462-024-10906-z.
[15] J. Zhou, J. Tian, R. Wang, Y. Wu, W. Xiao, and L. He, “SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis,” in Proceedings of the 28th International Conference on Computational Linguistics, 2020, pp. 568–579, doi: 10.18653/v1/2020.coling-main.49.
[16] R. Li, H. Chen, F. Feng, Z. Ma, X. Wang, and E. Hovy, “Dual graph convolutional networks for aspect-based sentiment analysis,” in ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference, 2021, pp. 6319–6329, doi: 10.18653/v1/2021.acl-long.494.
[17] W. Zhang, X. Li, Y. Deng, L. Bing, and W. Lam, “Towards Generative Aspect-Based Sentiment Analysis,” in ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference, 2021, vol. 2, pp. 504–510, doi: 10.18653/v1/2021.acl-short.64.
[18] C. Ouni, E. Benmohamed, and H. Ltifi, “Sentiment analysis deep learning model based on a novel hybrid embedding method,” Soc. Netw. Anal. Min., vol. 14, no. 1, p. 210, Oct. 2024, doi: 10.1007/s13278-024-01367-x.
[19] T. Osman, H. Khalil, M. Miltan, K. Shaalan, and R. Alfrjani, “Exploiting Functional Discourse Grammar to Enhance Complex Arabic Relation Extraction using a Hybrid Semantic Knowledge Base - Machine Learning Approach,” ACM Trans. Asian Low-Resource Lang. Inf. Process., vol. 22, no. 8, pp. 1–30, Aug. 2023, doi: 10.1145/3610581.
[20] B. F. Azevedo, A. M. A. C. Rocha, and A. I. Pereira, “Hybrid approaches to optimization and machine learning methods: a systematic literature review,” Mach. Learn., vol. 113, no. 7, pp. 4055–4097, Jul. 2024, doi: 10.1007/s10994-023-06467-x.
[21] H. Khalid, “Modern techniques in detecting, identifying and classifying fruits according to the developed machine learning algorithm,” J. Appl. Res. Technol., vol. 22, no. 2, pp. 219–229, 2024, doi: 10.22201/icat.24486736e.2024.22.2.2269.
[22] H. Khalid, “Efficient Image Annotation and Caption System Using Deep Convolutional Neural Networks,” BIO Web Conf., vol. 97, no. 3, p. 103, 2024, doi: 10.1051/bioconf/20249700103.
[23] H. J. Alantari, I. S. Currim, Y. Deng, and S. Singh, “An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews,” Int. J. Res. Mark., vol. 39, no. 1, pp. 1–19, Mar. 2022, doi: 10.1016/j.ijresmar.2021.10.011.
[24] M. Assaad and G. Shakah, “Optimizing Health Pattern Recognition Particle Swarm Optimization Approach for Enhanced Neural Network Performance(2):76-3. Available from: ,” Cihan Univ. Sci. J., vol. 8, no. 2, pp. 76–83, 2024, doi: 10.24086/cuesj.v8n2y2024.pp76-83.
[25] F. Husari and M. Assaad, “Intelligent Handwritten Identification Using Novel Hybrid Convolutional Neural Networks – Long-short-term Memory Architecture,” Cihan Univ. Sci. J., vol. 8, no. 2, pp. 99–103, 2024, doi: 10.24086/issn.2519-6979.
[26] H. O. Ahmad and S. U. Umar, “Sentiment Analysis of Financial Textual data Using Machine Learning and Deep Learning Models,” Informatica, vol. 47, pp. 153–158, 2023, doi: 10.31449/inf.v47i5.4673.
[27] Y. M. Hazzaa and S. U. Umar, “Improving Network Intrusion Detection with Convolutional Neural Networks and Data Balancing Techniques,” in Proceedings of Third International Conference on Computing and Communication Networks, 2024, pp. 675–687, doi: 10.1007/978-981-97-0892-5_53.
[28] O. Reda and A. Zellou, “Fulmqa: a fuzzy logic-based model for social media data quality assessment,” Soc. Netw. Anal. Min., vol. 13, no. 1, p. 150, Nov. 2023, doi: 10.1007/s13278-023-01148-y.
[29] A. H. Alamoodi et al., “Public Sentiment Analysis and Topic Modeling Regarding COVID-19’s Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysia,” KSII Trans. Internet Inf. Syst., vol. 16, no. 7, pp. 2169–2190, 2022, doi: 10.3837/tiis.2022.07.003.
[30] C. Vairetti, J. L. Assadi, and S. Maldonado, “Efficient hybrid oversampling and intelligent undersampling for imbalanced big data classification,” Expert Syst. Appl., vol. 246, p. 123149, Jul. 2024, doi: 10.1016/j.eswa.2024.123149.
[31] D. Elreedy, A. F. Atiya, and F. Kamalov, “A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning,” Mach. Learn., vol. 113, no. 7, pp. 4903–4923, Jul. 2024, doi: 10.1007/s10994-022-06296-4.
[32] B. V. Sekharreddy, V. N. Thatha, G. U. Kiran, V. Srilakshmi, and S. Sanapala, “A Survey On Text Classification Using Different Machine Learning Approaches,” in Futuristic Trends in Computing Technologies and Data Sciences Volume 3 Book 1, Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024, pp. 178–186, doi: 10.58532/V3BGCT1P5CH4.
[33] P. L. Rodriguez and A. Spirling, “Word Embeddings: What Works, What Doesn’t, and How to Tell the Difference for Applied Research,” J. Polit., vol. 84, no. 1, pp. 101–115, Jan. 2022, doi: 10.1086/715162.
[34] A. Boujamza and S. Lissane Elhaq, “Attention-based LSTM for Remaining Useful Life Estimation of Aircraft Engines,” IFAC-PapersOnLine, vol. 55, no. 12, pp. 450–455, Jan. 2022, doi: 10.1016/j.ifacol.2022.07.353.
[35] S. Nosouhian, F. Nosouhian, and A. Kazemi Khoshouei, “A Review of Recurrent Neural Network Architecture for Sequence Learning: Comparison between LSTM and GRU,” Preprints.org, no. July. pp. 1–7, Jul. 12, 2021, doi: 10.20944/preprints202107.0252.v1.
[36] N. Deshmukh, “Semi-Supervised Natural Language Processing Approach for Fine-Grained Classification of Medical Reports,” in 2019 IEEE MIT Undergraduate Research Technology Conference, URTC 2019, 2019, pp. 1–4, doi: 10.1109/URTC49097.2019.9660430.
[37] Z. Niu, G. Zhong, and H. Yu, “A review on the attention mechanism of deep learning,” Neurocomputing, vol. 452, pp. 48–62, Sep. 2021, doi: 10.1016/j.neucom.2021.03.091.
[38] A. Ahmadzade and S. Malekzadeh, “Spell Correction for Azerbaijani Language using Deep Neural Networks,” arXiv Prepr., vol. arXiv:2102, pp. 1–5, 2021, [Online]. Available at: https://arxiv.org/abs/2102.03218.
[39] S. Jamshidi et al., “Effective text classification using BERT, MTM LSTM, and DT,” Data Knowl. Eng., vol. 151, p. 102306, May 2024, doi: 10.1016/j.datak.2024.102306.
[40] S. Das, A. Tariq, T. Santos, S. S. Kantareddy, and I. Banerjee, “Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research,” in Neuromethods, vol. 197, Humana Press Inc., 2023, pp. 117–138, doi: 10.1007/978-1-0716-3195-9_4.
[41] M. K, A. Ramesh, R. G, S. Prem, R. A A, and D. M. P. Gopinath, “1D Convolution approach to human activity recognition using sensor data and comparison with machine learning algorithms,” Int. J. Cogn. Comput. Eng., vol. 2, pp. 130–143, 2021, doi: 10.1016/j.ijcce.2021.09.001.
[42] A. Zafar et al., “A Comparison of Pooling Methods for Convolutional Neural Networks,” Appl. Sci., vol. 12, no. 17, pp. 1–21, 2022, doi: 10.3390/app12178643.
[43] X. He, X. Wang, Z. Zhou, J. Wu, Z. Yang, and L. Thiele, “On-Device Deep Multi-Task Inference via Multi-Task Zipping,” IEEE Trans. Mob. Comput., vol. 22, no. 5, pp. 2878–2891, May 2023, doi: 10.1109/TMC.2021.3124306.
[44] S. Haghighi, M. Jasemi, S. Hessabi, and A. Zolanvari, “PyCM: Multiclass confusion matrix library in Python,” J. Open Source Softw., vol. 3, no. 25, p. 729, 2018, doi: 10.21105/joss.00729.
[45] I. Markoulidakis, I. Rallis, I. Georgoulas, G. Kopsiaftis, A. Doulamis, and N. Doulamis, “Multiclass Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem,” Technologies, vol. 9, no. 4, 2021, doi: 10.3390/technologies9040081.
[46] K. Roth, L. Pemula, J. Zepeda, B. Scholkopf, T. Brox, and P. Gehler, “Towards Total Recall in Industrial Anomaly Detection,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2022, vol. 2022-June, pp. 14298–14308, doi: 10.1109/CVPR52688.2022.01392.
[47] S. J. Maceachern and N. D. Forkert, “Machine learning for precision medicine,” Genome, vol. 64, no. 4, pp. 416–425, 2021, doi: 10.1139/gen-2020-0131.
[48] G. Fu et al., “A deep-learning-based approach for fast and robust steel surface defects classification,” Opt. Lasers Eng., vol. 121, pp. 397–405, 2019, doi: 10.1016/j.optlaseng.2019.05.005
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571 (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
andri.pranolo.id@ieee.org (publication issues)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0