Performance and Accuracy Enhancement of Machine Learning Model for Sentiment Analysis
Keywords:
Machine Learning (ML), Sentiment Analysis, Accuracy, Performance, Error rateAbstract
This research focuses on elevating the performance of machine learning models for sentiment analysis by concurrently addressing accuracy, error rate, and time consumption. Recognizing the critical importance of sentiment analysis in understanding user opinions and emotions expressed in textual data, our study proposes novel enhancements to overcome existing challenges. To improve accuracy, the research introduces a refined model architecture that incorporates attention mechanisms and contextual embeddings. These enhancements enable the model to capture nuanced relationships within the text, resulting in more precise sentiment predictions. Moreover, feature engineering techniques, including sentiment lexicons and domain-specific word embeddings, contribute to increased accuracy across diverse linguistic styles and specialized domains. Efforts to reduce error rates involve exploring advanced training methodologies, data augmentation, and transfer learning techniques. The model is rigorously evaluated on various datasets, demonstrating its enhanced generalization capabilities and robustness against varying linguistic nuances. In addressing time consumption concerns, optimization strategies are employed to streamline computational processes without compromising accuracy. Efficient model training and inference contribute to a notable reduction in processing time, making the proposed model suitable for real-time sentiment analysis applications. The research findings are validated through extensive experiments, comparing the enhanced model against state-of-the-art sentiment analysis approaches. Results indicate significant improvements in accuracy, a reduction in error rates, and enhanced computational efficiency, making the proposed model a compelling choice for practical deployment in diverse application domains. In conclusion, this research presents a comprehensive enhancement framework for sentiment analysis models, striking a balance between accuracy, error rate reduction, and efficient time consumption. The proposed model not only advances the current approach but also offers a practical and effective solution for real-world sentiment analysis applications.
Downloads
References
M. M. Hamed Taherdoost 1, “Artificial Intelligence and Sentiment Analysis : A Review in,” Comput. 2023, 12, 37., no. 0, 2023. https// doi.org/10.3390/computers12020037
S. K. Assayed, K. Shaalan, M. Alkhatib, and S. Maghaydah, “Machine Learning Chatbot for Sentiment Analysis of Covid-19 Tweets,” pp. 41–55, 2023, doi: 10.5121/csit.2023.130404.
N. Braig, A. Benz, S. Voth, J. Breitenbach, and R. Buettner, “Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data,” IEEE Access, vol. 11, no. February, pp. 14778–14803, 2023, doi: 10.1109/ACCESS.2023.3242234.
H. Rahman, J. Tariq, M. A. Masood, A. F. Subahi, O. I. Khalaf, and Y. Alotaibi, “Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning,” Comput. Mater. Contin., vol. 74, no. 3, pp. 5527–5543, 2023, doi: 10.32604/cmc.2023.033190.
L. H. B, Proceedings of the 2022 7th International Conference on Modern Management and Education Technology (MMET 2022). Atlantis Press SARL, 2023. doi: 10.2991/978-2-494069-51-0.
M. S. BAŞARSLAN and F. KAYAALP, “Sentiment analysis with ensemble and machine learning methods in multi-domain datasets,” Turkish J. Eng., vol. 7, no. 2, pp. 141–148, 2023, doi: 10.31127/tuje.1079698.
M. Bordoloi and S. K. Biswas, Sentiment analysis: A survey on design framework, applications and future scopes, no. 0123456789. Springer Netherlands, 2023. doi: 10.1007/s10462-023-10442-2.
A. Quazi and M. K. Srivastava, “Twitter Sentiment Analysis Using Machine Learning,” Lect. Notes Electr. Eng., vol. 877, no. 05, pp. 379–389, 2023, doi: 10.1007/978-981-19-0312-0_38.
C.- Reviews, C. Singh, T. Imam, and S. Wibowo, “applied sciences A Deep Learning Approach for Sentiment Analysis of,” 2022.
M. Saraiva, I. Matijošaitienė, S. Mishra, and A. Amante, “Crime Prediction and Monitoring in Porto, Portugal, Using Machine Learning, Spatial and Text Analytics,” ISPRS Int. J. Geo-Information, vol. 11, no. 7, 2022, doi: 10.3390/ijgi11070400.
A. Joshi, B. Akash, R. Bharathkhanna, and T. Srihari, “Improved Comment Sentiment Analysis Method Using Deep Learning,” vol. 10, no. 5, pp. 722–726, 2022.
A. Alsayat, “Improving Sentiment Analysis for Social Media Applications Using an Ensemble Deep Learning Language Model,” Arab. J. Sci. Eng., vol. 47, no. 2, pp. 2499–2511, 2022, doi: 10.1007/s13369-021-06227-w.
Ezenwobodo and S. Samuel, “International Journal of Research Publication and Reviews,” Int. J. Res. Publ. Rev., vol. 04, no. 01, pp. 1806–1812, 2022, doi: 10.55248/gengpi.2023.4149.
A. Motz, E. Ranta, A. S. Calderon, Q. Adam, F. Alzhouri, and D. Ebrahimi, “Live Sentiment Analysis Using Multiple Machine Learning and Text Processing Algorithms,” Procedia Comput. Sci., vol. 203, pp. 165–172, 2022, doi: 10.1016/j.procs.2022.07.023.
D. Geethangili, “Machine Learning Approach based Sentiment Analysis, Classification: An Application of Natural Language Processing,” vol. 71, no. 4, pp. 773–786, 2022, [Online]. Available: http://philstat.org.ph
G. I. Ahmad, J. Singla, A. Ali, A. A. Reshi, and A. A. Salameh, “Machine Learning Techniques for Sentiment Analysis of Code-Mixed and Switched Indian Social Media Text Corpus: A Comprehensive Review,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 2, pp. 455–467, 2022, doi: 10.14569/IJACSA.2022.0130254.
A. P. Rodrigues et al., “Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/5211949.
A. Yenkikar, C. N. Babu, and D. J. Hemanth, “Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble,” PeerJ Comput. Sci., vol. 8, pp. 1–34, 2022, doi: 10.7717/PEERJ-CS.1100.
C. Chen, B. Xu, J. H. Yang, and M. Liu, “Sentiment Analysis of Animated Film Reviews Using Intelligent Machine Learning,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/8517205.
P. A. Grana, “Sentiment Analysis of Text Using Machine Learning Models,” Int. Res. J. Mod. Eng. Technol. Sci., no. 05, pp. 2582–5208, 2022, [Online]. Available: www.irjmets.com
D. Panchal, M. Mehta, A. Mishra, S. Ghole, and M. S. Dandge, “Sentiment Analysis Using Natural Language Processing,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 10, no. 5, pp. 2262–2266, 2022, doi: 10.22214/ijraset.2022.42711.
K. L. Tan, C. P. Lee, K. M. Lim, and K. S. M. Anbananthen, “Sentiment Analysis With Ensemble Hybrid Deep Learning Model,” IEEE Access, vol. 10, no. September, pp. 103694–103704, 2022, doi: 10.1109/ACCESS.2022.3210182.
S. Kumar, K. Sharma, D. Veragi, and A. Juyal, “Sentimental Analysis of Movie Reviews Using Machine Learning Algorithms,” 2022 Int. Conf. Mach. Learn. Big Data, Cloud Parallel Comput. COM-IT-CON 2022, vol. 02006, pp. 526–529, 2022, doi: 10.1109/COM-IT-CON54601.2022.9850878.
S. Sahu, K. Divya, D. N. Rastogi, P. K. Yadav, and D. Y. Perwej, “Sentimental Analysis on Web Scraping Using Machine Learning Method,” J. Inf. Comput. Sci., vol. 12, no. 8, 2022, doi: 10.12733/JICS.2022/V12I08.535569.67004.
J. R. Monalisha Sahoo, “Survey on Sentiment Analysis to Predict Twitter Data using Machine Learning and Deep Learning,” Int. J. Eng. Res. Technol., vol. 11, no. 7, pp. 506–512, 2022.
V. Talukdar, D. Dhabliya, B. Kumar, S. B. Talukdar, S. Ahamad, and A. Gupta, “Suspicious Activity Detection and Classification in IoT Environment Using Machine Learning Approach,” 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, Nov. 25, 2022. doi: 10.1109/pdgc56933.2022.10053312.
P. R. Kshirsagar, D. H. Reddy, M. Dhingra, D. Dhabliya, and A. Gupta, “A Scalable Platform to Collect, Store, Visualize and Analyze Big Data in Real- Time,” 2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM). IEEE, Feb. 22, 2023. doi: 10.1109/iciptm57143.2023.10118183.
M. Dhingra, D. Dhabliya, M. K. Dubey, A. Gupta, and D. H. Reddy, “A Review on Comparison of Machine Learning Algorithms for Text Classification,” 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, Dec. 14, 2022. doi: 10.1109/ic3i56241.2022.10072502.
D. Mandal, K. A. Shukla, A. Ghosh, A. Gupta, and D. Dhabliya, “Molecular Dynamics Simulation for Serial and Parallel Computation Using Leaf Frog Algorithm,” 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, Nov. 25, 2022. doi: 10.1109/pdgc56933.2022.10053161.
P. R. Kshirsagar, D. H. Reddy, M. Dhingra, D. Dhabliya, and A. Gupta, “A Review on Application of Deep Learning in Natural Language Processing,” 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, Dec. 14, 2022. doi: 10.1109/ic3i56241.2022.10073309.
P. R. Kshirsagar, D. H. Reddy, M. Dhingra, D. Dhabliya, and A. Gupta, “Detection of Liver Disease Using Machine Learning Approach,” 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, Dec. 14, 2022. doi: 10.1109/ic3i56241.2022.10073425.
V. V. Chellam, S. Praveenkumar, S. B. Talukdar, V. Talukdar, S. K. Jain, and A. Gupta, “Development of a Blockchain-based Platform to Simplify the Sharing of Patient Data,” 2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM). IEEE, Feb. 22, 2023. doi: 10.1109/iciptm57143.2023.10118194.
P. Lalitha Kumari et al., “Methodology for Classifying Objects in High-Resolution Optical Images Using Deep Learning Techniques,” Lecture Notes in Electrical Engineering. Springer Nature Singapore, pp. 619–629, 2023. doi: 10.1007/978-981-19-8865-3_55.
N. Sindhwani et al., “Comparative Analysis of Optimization Algorithms for Antenna Selection in MIMO Systems,” Lecture Notes in Electrical Engineering. Springer Nature Singapore, pp. 607–617, 2023. doi: 10.1007/978-981-19-8865-3_54.
V. Jain, S. M. Beram, V. Talukdar, T. Patil, D. Dhabliya, and A. Gupta, “Accuracy Enhancement in Machine Learning During Blockchain Based Transaction Classification,” 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, Nov. 25, 2022. doi: 10.1109/pdgc56933.2022.10053213.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
