Adaptive Particle Grey Wolf Optimizer with Deep Learning-based Sentiment Analysis on Online Product Reviews
Received: 16 February 2023 | Revised: 16 March 2023 | Accepted: 23 March 2023 | Online: 2 June 2023
Corresponding author: Durai Elangovan
Abstract
The increasing use of e-commerce websites and social networks is continually generating an immense amount of data in various forms, such as text, images or sounds, videos, etc. Sentiment analysis (SA) in online product reviews is a method of identifying the overall sentiment of customers about a specific product or service. This study used Natural Language Processing (NLP) and Machine Learning (ML) algorithms to identify and extract opinions and emotions expressed in text. Online reviews are often written in informal language, slang, and dialects, making it difficult for ML models to accurately classify sentiments. In addition, the use of misspelled words or incorrect grammar can further complicate the analysis. The recent developments of Deep Learning (DL) models can be used for the accurate classification of sentiments. This paper presents an Adaptive Particle Grey Wolf Optimizer with Deep Learning Based Sentiment Analysis (APGWO-DLSA) method to accurately classify sentiments in product reviews. Initially, data pre-processing was performed to improve the quality of the product reviews using the word2vec embedding process. For sentiment classification, the proposed method used a Deep Belief Network (DBN) model. Finally, the hyperparameter tuning of the DBN was performed using the APGWO algorithm. An extensive experimental analysis demonstrated the improved results of APGWO-DLSA over other methods, showing a maximum accuracy of 94.77% and 85.31% on the Cell Phones And Accessories (CPAA) and Amazon Products (AP) datasets.
Keywords:
sentiment analysis, online product reviews, machine learning, deep learning, natural language processingDownloads
References
R. S. Jagdale, V. S. Shirsat, and S. N. Deshmukh, "Sentiment Analysis on Product Reviews Using Machine Learning Techniques," in Cognitive Informatics and Soft Computing, Singapore, 2019, pp. 639–647. DOI: https://doi.org/10.1007/978-981-13-0617-4_61
L. Yang, Y. Li, J. Wang, and R. S. Sherratt, "Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning," IEEE Access, vol. 8, pp. 23522–23530, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2969854
A. Onan, "Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks," Concurrency and Computation: Practice and Experience, vol. 33, no. 23, 2021, Art. no. e5909. DOI: https://doi.org/10.1002/cpe.5909
R. S. S. Singh, T. J. S. Anand, S. A. Anas, and B. Acharya, "A Real-Time Analytic Face Thermal Recognition System Integrated with Email Notification," Engineering, Technology & Applied Science Research, vol. 13, no. 1, pp. 9961–9967, Feb. 2023. DOI: https://doi.org/10.48084/etasr.5430
K. Aldriwish, "A Deep Learning Approach for Malware and Software Piracy Threat Detection," Engineering, Technology & Applied Science Research, vol. 11, no. 6, pp. 7757–7762, Dec. 2021. DOI: https://doi.org/10.48084/etasr.4412
T. Akhtar, N. G. Haider, and S. M. Khan, "A Comparative Study of the Application of Glowworm Swarm Optimization Algorithm with other Nature-Inspired Algorithms in the Network Load Balancing Problem," Engineering, Technology & Applied Science Research, vol. 12, no. 4, pp. 8777–8784, Aug. 2022. DOI: https://doi.org/10.48084/etasr.4999
M. Bhalekar and M. Bedekar, "The New Dataset MITWPU-1K for Object Recognition and Image Captioning Tasks," Engineering, Technology & Applied Science Research, vol. 12, no. 4, pp. 8803–8808, Aug. 2022. DOI: https://doi.org/10.48084/etasr.5039
M. A. Fauzi, "Word2Vec model for sentiment analysis of product reviews in Indonesian language," International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 1, pp. 525–530, Feb. 2019. DOI: https://doi.org/10.11591/ijece.v9i1.pp525-530
P. Verma, A. Dumka, A. Bhardwaj, and A. Ashok, "Product Review-Based Customer Sentiment Analysis Using an Ensemble of mRMR and Forest Optimization Algorithm (FOA)," International Journal of Applied Metaheuristic Computing (IJAMC), vol. 13, no. 1, pp. 1–21, Jan. 2022. DOI: https://doi.org/10.4018/IJAMC.2022010107
S. Sindhura, S. P. Praveen, M. A. Safali, and N. Rao, "Sentiment Analysis for Product Reviews Based on Weakly-Supervised Deep Embedding," in 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, Sep. 2021, pp. 999–1004. DOI: https://doi.org/10.1109/ICIRCA51532.2021.9544985
G. Xu, Y. Meng, X. Qiu, Z. Yu, and X. Wu, "Sentiment Analysis of Comment Texts Based on BiLSTM," IEEE Access, vol. 7, pp. 51522–51532, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2909919
T. Gu, H. Zhao, Z. He, M. Li, and D. Ying, "Integrating external knowledge into aspect-based sentiment analysis using graph neural network," Knowledge-Based Systems, vol. 259, Jan. 2023, Art. no. 110025. DOI: https://doi.org/10.1016/j.knosys.2022.110025
A. Thakkar, D. Mungra, A. Agrawal, and K. Chaudhari, "Improving the Performance of Sentiment Analysis Using Enhanced Preprocessing Technique and Artificial Neural Network," IEEE Transactions on Affective Computing, vol. 13, no. 4, pp. 1771–1782, Jul. 2022. DOI: https://doi.org/10.1109/TAFFC.2022.3206891
D. K. Jain, P. Boyapati, J. Venkatesh, and M. Prakash, "An Intelligent Cognitive-Inspired Computing with Big Data Analytics Framework for Sentiment Analysis and Classification," Information Processing & Management, vol. 59, no. 1, Jan. 2022, Art. no. 102758. DOI: https://doi.org/10.1016/j.ipm.2021.102758
A. A. A. Ahmed, S. Agarwal, Im. G. A. Kurniawan, S. P. D. Anantadjaya, and C. Krishnan, "Business boosting through sentiment analysis using Artificial Intelligence approach," International Journal of System Assurance Engineering and Management, vol. 13, no. 1, pp. 699–709, Mar. 2022. DOI: https://doi.org/10.1007/s13198-021-01594-x
M. F. Tsai and J. Y. Huang, "Sentiment analysis of pets using deep learning technologies in artificial intelligence of things system," Soft Computing, vol. 25, no. 21, pp. 13741–13752, Nov. 2021. DOI: https://doi.org/10.1007/s00500-021-06038-z
M. Mujahid et al., "Sentiment Analysis and Topic Modeling on Tweets about Online Education during COVID-19," Applied Sciences, vol. 11, no. 18, Jan. 2021, Art. no. 8438. DOI: https://doi.org/10.3390/app11188438
Z. H. Kilimci, S. Akyokus, and I. Czarnowski, "Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification," Complexity, vol. 2018, Jan. 2018. DOI: https://doi.org/10.1155/2018/7130146
A. A. Süzen, "Developing a multi-level intrusion detection system using hybrid-DBN," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 2, pp. 1913–1923, Feb. 2021. DOI: https://doi.org/10.1007/s12652-020-02271-w
T. T. M. Huynh, T. M. Le, L. T. That, L. V. Tran, and S. V. T. Dao, "A Two-Stage Feature Selection Approach for Fruit Recognition Using Camera Images With Various Machine Learning Classifiers," IEEE Access, vol. 10, pp. 132260–132270, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3227712
A. Iqbal, R. Amin, J. Iqbal, R. Alroobaea, A. Binmahfoudh, and M. Hussain, "Sentiment Analysis of Consumer Reviews Using Deep Learning," Sustainability, vol. 14, no. 17, Art. no. 10844, Jan. 2022. DOI: https://doi.org/10.3390/su141710844
Downloads
How to Cite
License
Copyright (c) 2023 Durai Elangovan, Varatharaj Subedha
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
