Design and Development of an Efficient Ensemble Model for Aspect-Based Sentiment Analysis
Received: 27 March 2025 | Revised: 26 April 2025 and 11 May 2025 | Accepted: 15 May 2025 | Online: 2 August 2025
Corresponding author: Ayesha Siddiqua
Abstract
Aspect-Based Sentiment Analysis (ABSA), a subfield of Natural Language Processing (NLP), aims to identify the sentiment polarity of particular features within social media content or text reviews. Unlike conventional sentiment analysis, which categorizes a whole text as positive, negative, or neutral, ABSA connects emotions to specific qualities, therefore providing more detailed insights useful for companies, product creators, and academics. Using a dataset of user-generated reviews, each tagged with aspect terms and their locations, this study focuses on aspect-term polarity estimation. Major difficulties include recording changes in context-dependent emotion caused by the surrounding language and word choices. Initially, conventional machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbor (KNN) were used. Acknowledging the shortcomings of single models, such as feature dependency and overfitting, this work also uses an ensemble learning technique with voting classifiers, investigating both hard (majority rule) and soft voting (probabilistic averaging). Experimental findings show that these ensemble-based voting classifiers regularly beat individual models, attaining better sentiment classification accuracy and increasing model robustness. The results confirm the efficiency of ensemble methods in promoting ABSA and enhancing sentiment analysis for practical use.
Keywords:
Aspect-Based Sentiment Analysis (ABSA), NLP, RF, DT, kNN, SVM, voting-based ensemble learningDownloads
References
C. Lohith, H. Chandramouli, U. Balasingam, and S. Arun Kumar, "Aspect Oriented Sentiment Analysis on Customer Reviews on Restaurant Using the LDA and BERT Method," SN Computer Science, vol. 4, no. 4, May 2023, Art. no. 399. DOI: https://doi.org/10.1007/s42979-022-01634-8
K. Aurangzeb, N. Ayub, and M. Alhussein, "Aspect Based Multi-Labeling Using SVM Based Ensembler," IEEE Access, vol. 9, pp. 26026–26040, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3055768
Y. Zhang, J. Du, X. Ma, H. Wen, and G. Fortino, "Aspect-Based Sentiment Analysis for User Reviews," Cognitive Computation, vol. 13, no. 5, pp. 1114–1127, Sep. 2021. DOI: https://doi.org/10.1007/s12559-021-09855-4
G. Kaur and A. Sharma, "A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis," Journal of Big Data, vol. 10, no. 1, Jan. 2023, Art. no. 5. DOI: https://doi.org/10.1186/s40537-022-00680-6
M. S. Alamanda, "Aspect-based sentiment analysis search engine for social media data," CSI Transactions on ICT, vol. 8, no. 2, pp. 193–197, Jun. 2020. DOI: https://doi.org/10.1007/s40012-020-00295-3
S. Al-Dabet, S. Tedmori, and M. AL-Smadi, "Enhancing Arabic aspect-based sentiment analysis using deep learning models," Computer Speech & Language, vol. 69, Sep. 2021, Art. no. 101224. DOI: https://doi.org/10.1016/j.csl.2021.101224
S. Kotagiri, A. M. Sowjanya, B. Anilkumar, and N. L. Devi, "Aspect-oriented extraction and sentiment analysis using optimized hybrid deep learning approaches," Multimedia Tools and Applications, vol. 83, no. 41, pp. 88613–88644, Mar. 2024. DOI: https://doi.org/10.1007/s11042-024-18964-9
K. Ahmed et al., "Breaking down linguistic complexities: A structured approach to aspect-based sentiment analysis," Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 8, Sep. 2023, Art. no. 101651. DOI: https://doi.org/10.1016/j.jksuci.2023.101651
R. Shad, K. Potter, and A. Gracias, "Natural Language Processing (NLP) for Sentiment Analysis: A Comparative Study of Machine Learning Algorithms." Preprints.org, Oct. 30, 2024. DOI: https://doi.org/10.20944/preprints202410.2338.v1
A. Maroof, S. Wasi, S. I. Jami, and M. S. Siddiqui, "Aspect-Based Sentiment Analysis for Service Industry," IEEE Access, vol. 12, pp. 109702–109713, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3440357
Y. C. Hua, P. Denny, J. Wicker, and K. Taskova, "A systematic review of aspect-based sentiment analysis: domains, methods, and trends," Artificial Intelligence Review, vol. 57, no. 11, Sep. 2024, Art. no. 296. DOI: https://doi.org/10.1007/s10462-024-10906-z
A. Pathak, S. Kumar, P. Roy, and B. G. Kim, "Aspect-Based Sentiment Analysis in Hindi Language by Ensembling Pre-Trained mBERT Models," Electronics, vol. 10, no. 21, Oct. 2021, Art. no. 2641. DOI: https://doi.org/10.3390/electronics10212641
S. Hammi, S. M. Hammami, and L. H. Belguith, "Advancing aspect-based sentiment analysis with a novel architecture combining deep learning models CNN and bi-RNN with the machine learning model SVM," Social Network Analysis and Mining, vol. 13, no. 1, Sep. 2023, Art. no. 117. DOI: https://doi.org/10.1007/s13278-023-01126-4
H. Zhang and M. O. Shafiq, "Survey of transformers and towards ensemble learning using transformers for natural language processing," Journal of Big Data, vol. 11, no. 1, Feb. 2024, Art. no. 25. DOI: https://doi.org/10.1186/s40537-023-00842-0
K. Mishev, A. Gjorgjevikj, I. Vodenska, L. T. Chitkushev, and D. Trajanov, "Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers," IEEE Access, vol. 8, pp. 131662–131682, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3009626
M. S. Akhtar, A. Ekbal, and P. Bhattacharyya, "Aspect Based Sentiment Analysis: Category Detection and Sentiment Classification for Hindi," in Computational Linguistics and Intelligent Text Processing, vol. 9624, A. Gelbukh, Ed. Springer International Publishing, 2018, pp. 246–257. DOI: https://doi.org/10.1007/978-3-319-75487-1_19
M. S. Akhtar, T. Garg, and A. Ekbal, "Multi-task learning for aspect term extraction and aspect sentiment classification," Neurocomputing, vol. 398, pp. 247–256, Jul. 2020. DOI: https://doi.org/10.1016/j.neucom.2020.02.093
M. R. R. Rana, A. Nawaz, T. Ali, A. M. El-Sherbeeny, and W. Ali, "A BiLSTM-CF and BiGRU-based Deep Sentiment Analysis Model to Explore Customer Reviews for Effective Recommendations," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11739–11746, Oct. 2023. DOI: https://doi.org/10.48084/etasr.6278
M. Z. Asghar, A. Khan, S. R. Zahra, S. Ahmad, and F. M. Kundi, "Aspect-based opinion mining framework using heuristic patterns," Cluster Computing, vol. 22, no. S3, pp. 7181–7199, May 2019. DOI: https://doi.org/10.1007/s10586-017-1096-9
M. Pontiki et al., "SemEval-2016 Task 5: Aspect Based Sentiment Analysis," in 10th International Workshop on Semantic Evaluation (SemEval 2016), San Diego, CA, USA, Mar. 2016, pp. 19–30. DOI: https://doi.org/10.18653/v1/S16-1002
Downloads
How to Cite
License
Copyright (c) 2025 Ayesha Siddiqua, H. C. Nagaraj
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.
