Sentiment Analysis for Prediction of Brand Value Using Albert Model
Keywords:
Sentiment Analysis (SA), BERT (Bidirectional Encoder Representations from Transformers), ALBERT (A Lite BERT), Customer Reviews, Supply Chain Management, Brand Value, Dependency Parsing, Part of Speech (POS).Abstract
In today's dynamic and global business environment, organizations face the challenge of meeting customer expectations while effectively managing their supply chain. Understanding customer demands and accurately getting product sales is critical to achieve this. The effectiveness of the existing “BERT” in sentiment analysis is well established and its resource-intensive models might face challenges in deployment, especially in scenarios with constraints on computational resources. This study explores the use of sentiment analysis and the ALBERT model to predict brand value based on customer reviews. Both BERT and ALBERT models are more powerful, but ALBERT offers a more efficient alternative without compromising performance, making it particularly appealing for tasks where computational efficiency is a priority. The proposed approach combines various techniques, including tokenization, POS tagging, and dependency parsing, to improve the accuracy of SA models. This study not only establishes the effectiveness of transformer architectures in sentiment analysis but also helps the advancement of brand valuation approaches. The findings have impacts on marketers, as they provide a powerful tool for assessing customer sentiment and obtaining brand value with unprecedented accuracy. The results of the proposed model outperform with 95.98% of accuracy, 96.72 % of precision, 94.38% of recall, and 95.53% of F-measure.
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X. Zhang, & J. S. L. Lam, “Measuring the Impact of E-Collaboration on Supply Chain Parties: A Value-Based Management Approach,” IEEE Access, vol. 9, pp. 118181-118193, 2021.
S. Ammirato, R. Linzalone, & A. M. Felicetti, “Business model innovation drivers as antecedents of performance,” Measuring business excellence, vol. 26, no. 1, pp. 6-22, 2022.
P. Sánchez-Núñez, M. J. Cobo, C. De las Heras-Pedrosa, J. I. Pelaez, & E. Herrera-Viedma, “Opinion mining, sentiment analysis, and emotion understanding in advertising: a bibliometric analysis,” IEEE Access,vol. 8, pp. 134563-134576, 2020.
C. S. Wu, A. Madotto, W. Liu, P. Fung, & C. Xiong, “Qaconv: Question answering on informative conversations,” arXiv preprint arXiv:2105.06912, 2021.
K. Kim, S. Yoon, & Y. K. Choi, “The effects of eWOM volume and valence on product sales–an empirical examination of the movie industry,” International Journal of Advertising, vol. 38, no. 3, pp. 471-488, 2019.
M. Wankhade, A. C. S. Rao, & C. Kulkarni, “A survey on sentiment analysis methods, applications, and challenges,” Artificial Intelligence Review, vol. 55, no. 7, pp. 5731-5780, 2022.
S. Sachin, A. Tripathi, N. Mahajan, S. Aggarwal, & P. Nagrath, “Sentiment analysis using gated recurrent neural networks,” SN Computer Science, vol.1, pp. 1-13, 2020.
A. Amin, I. Hossain, A. Akther, & K. M. Alam, “Bengali vader: A sentiment analysis approach using modified vader,” In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1-6. IEEE, 2019, February.
T. N. Prakash, & A. Aloysius, “A Comparative Study of Lexicon-based and Machine learning based classifications in Sentiment analysis,” International Journal of Data Mining Techniques and Applications, vol. 8, no. 1, pp. 43-47, 2019.
B. Liu, Sentiment analysis and opinion mining. Springer Nature, 2022.
R. Catelli, S. Pelosi, & M. Esposito, “Lexicon-based vs. Bert-based sentiment analysis: A comparative study in Italian,” Electronics, vol. 11, no. 3, pp. 374, 2022.
R. S. Jagdale, V. S. Shirsat, & S. N. Deshmukh, “Sentiment analysis on product reviews using machine learning techniques,” In Cognitive Informatics and Soft Computing: Proceeding of CISC 2017, pp. 639-647. Springer Singapore, 2019.
M. Guia, R. R. Silva, & J. Bernardino, “Comparison of Naïve Bayes, Support Vector Machine, Decision Trees, and Random Forest on Sentiment Analysis,” KDIR, vol. 1, pp. 525-531, 2019.
A. Dadhich, & B. Thankachan, “Sentiment analysis of Amazon product reviews using a hybrid rule-based approach,” In Smart Systems: Innovations in Computing: Proceedings of SSIC 2021, pp. 173-193. Springer Singapore, 2022.
N. Shrestha, & F. Nasoz, “Deep learning sentiment analysis of Amazon.com reviews and ratings,” arXiv preprint arXiv:1904.04096, 2019.
C. Du, H. Sun, J. Wang, Q. Qi, & J. Liao, “Adversarial and domain-aware BERT for cross-domain sentiment analysis,” In Proceedings of the 58th annual meeting of the Association for Computational Linguistics, pp. 4019-4028, 2020, July.
Z. Desai, K. Anklesaria, & H. Balasubramaniam, “Business Intelligence Visualization Using Deep Learning Based Sentiment Analysis on Amazon Review Data,” In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1-7. IEEE, 2021, July.
A. S. AlQahtani, “Product sentiment analysis for Amazon reviews,” International Journal of Computer Science & Information Technology (IJCSIT) vol. 13, 2021.
M. P. Geetha, & D. K. Renuka, (2021). “Improving the performance of aspect-based sentiment analysis using a fine-tuned Bert Base Uncased model,” International Journal of Intelligent Networks, vol. 2, pp. 64-69.
M. Mostafa, & A. AlSaeed, “Sentiment Analysis Based on Bert for Amazon Reviewer,” Journal of the ACS Advances in Computer Science, vol. 13, no. 1, pp. 1-10, 2022.
A. Iqbal, R. Amin, J. Iqbal, R. Alroobaea, A. Binmahfoudh, & M. Hussain, (2022). “Sentiment Analysis of Consumer Reviews Using Deep Learning,” Sustainability, vol. 14, no. 17, pp. 10844.
J. Mutinda, W. Mwangi, & G. Okeyo, “Sentiment analysis of text reviews using lexicon-enhanced Bert embedding (LeBERT) model with convolutional neural network,” Applied Sciences, vol. 13, no. 3, pp. 1445, 2023.
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