International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 16, No. 3, 8 Jun. 2026
Cover page and Table of Contents: PDF (size: 1655KB)
Benchmarking Linear, Ensemble, and Neural Models for Entity-Level Sentiment Analysis on Twitter Data
PDF (1655KB), PP.365-382
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Author(s)
Kavita R. Shelke
1,*
Malcolm Alex Raj
1
Darshana S. Gajbhiye
2
Rakhi D. Akhare
3
Index Terms
Entity-Level Sentiment Analysis, Social Media Analytics, Machine Learning, Natural Language Processing, Twitter Data Mining
Abstract
Social media streams reflect opinion of public in real-time, but short and noisy tweets make it hard to attribute sentiment to entities and this paper introduces an AI/ML pipeline to classify the sentiment towards the referenced entities in twitter messages. Using the Twitter Entity Sentiment Analysis benchmark (twitter_training.csv and twitter_validation.csv), the tweets are normalized (lowercasing, punctuation, platform specific artifacts, tokenization, stop word filtering and lemmatization) and represented using TF-IDF (Term Frequency–Inverse Document Frequency) features with a maximum of 5000 terms. Machine learning models including Logistic Regression, linear SVM, Multinomial Naive Bayes, and ensemble and neural methods such as Random Forest, XGBoost, and Multilayer Perceptron (MLP) are trained on the training split and evaluated on the validation split using macro-averaged precision, recall, F1-score, and confusion matrix analysis. The results show that linear discriminative models are well suited to sparse TF-IDF spaces, with SVM and Logistic Regression providing balanced class-wise behaviour and Naive Bayes offering a computationally efficient baseline. XGBoost delivers moderate improvements over simple probabilistic models, while Random Forest achieves substantial gains through ensemble learning. The best overall performance is obtained by MLP, demonstrating that non-linear neural modeling more effectively captures complex feature interactions and entity-relevance patterns. Misclassifications are focused on the Neutral - Irrelevant boundary, resulting in instances of relevance ambiguity at the entity level in the case of sentiment and driving future extensions with context aware deep architectures and entity conditioned representations. These baselines provide support for monitoring purposes for brands and public figures as well as expose the limits of non-contextual features for sarcasm and implicit targets in Twitter discourse.
Cite This Paper
Kavita R. Shelke, Malcolm Alex Raj, Darshana S. Gajbhiye, Rakhi D. Akhare, "Benchmarking Linear, Ensemble, and Neural Models for Entity-Level Sentiment Analysis on Twitter Data", International Journal of Engineering and Manufacturing (IJEM), Vol.16, No.3, pp.365-382, 2026. DOI:10.5815/ijem.2026.03.22
Reference
[1]Y. Hariprasad, S. Lokesh, N. T. Sharathkumar, L. K. KJ, C. Miller, and N. K. Chaudhary, "AI-ML analytics: A comprehensive investigation on sentimental analysis for social media forensics textual data," in Science and Information Conference, Cham, Switzerland, Jul. 2023, pp. 923-935, Springer Nature Switzerland. DOI: 10.1007/978-3-031-37963-5_64
[2]A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, and M. C. Onuoha, "AI-Based Techniques for Online Social Media Network Sentiment Analysis: A Methodical Review," Int. J. Comput. Inf. Eng., vol. 16, no. 12, pp. 550-560, 2022.
[3]N. V. Babu and E. G. M. Kanaga, "Sentiment analysis in social media data for depression detection using artificial intelligence: A review," SN Comput. Sci., vol. 3, no. 1, p. 74, 2022. DOI: 10.1007/s42979-021-00958-1
[4]T. Babu, R. Sharma, K. S. K. Rani, A. Sungheetha, B. Priyadarshini, and A. Nivetha, "AI Powered Sentiment Analysis of Social Media Presence," in Proc. 2024 Second Int. Conf. on Advances in Information Technology (ICAIT), vol. 1, pp. 1-5, 2024. DOI: 10.1109/ICAIT61638.2024.10690773
[5]B. Jamalpur, S. H. Krishna, R. Bhat, S. B. Prasad, S. Bhattacharya, and D. Kapila, "AI in Social Media Marketing: Using Machine Learning to Analyze Trends and Predict Consumer Sentiment," in Proc. 2025 Int. Conf. on Pervasive Computational Technologies (ICPCT), pp. 890-894, 2025.DOI: 10.1109/ICAIT61638.2024.10690773
[6]C. Qian, N. Mathur, N. H. Zakaria, R. Arora, V. Gupta, and M. Ali, "Understanding public opinions on social media for financial sentiment analysis using AI-based techniques," Inf. Process. Manage., vol. 59, no. 6, p. 103098, 2022. https://doi.org/10.1016/j.ipm.2022.103098
[7]Y. Qi and Z. Shabrina, "Sentiment analysis using Twitter data: A comparative application of lexicon-and machine-learning-based approach," Social Netw. Anal. Min., vol. 13, no. 1, p. 31, 2023. https://doi.org/10.1007/s13278-023-01030-x
[8]B. B. Budisusetyo, W. A. Dhitya, A. Nugroho, and G. Pangestu, "Sentiment analysis methods recommendation: A review of AI-based techniques on social media analysis," Procedia Comput. Sci., vol. 245, pp. 1120-1128, 2024. https://doi.org/10.1016/j.procs.2024.10.341
[9]G. Revathy, S. A. Alghamdi, S. M. Alahmari, S. R. Yonbawi, A. Kumar, and M. A. Haq, "Sentiment analysis using machine learning: Progress in the machine intelligence for data science," Sustain. Energy Technol. Assess., vol. 53, p. 102557, 2022. https://doi.org/10.1016/j.seta.2022.102557
[10]A. Alsayat, "Improving sentiment analysis for social media applications using an ensemble deep learning language model," Arabian J. Sci. Eng., vol. 47, no. 2, pp. 2499-2511, 2022. https://doi.org/10.1007/s13369-021-06227-w
[11]A. A. A. Ahmed, S. Agarwal, I. G. A. Kurniawan, S. P. Anantadjaya, and C. Krishnan, "Business boosting through sentiment analysis using Artificial Intelligence approach," Int. J. Syst. Assur. Eng. Manag., vol. 13, Suppl. 1, pp. 699-709, 2022. https://doi.org/10.1007/s13198-021-01594-x
[12]A. Alslaity and R. Orji, "Machine learning techniques for emotion detection and sentiment analysis: Current state, challenges, and future directions," Behav. Inf. Technol., vol. 43, no. 1, pp. 139-164, 2024.
[13]S. H. Biradar, J. V. Gorabal, and G. Gupta, "Machine learning tool for exploring sentiment analysis on Twitter data," Mater. Today Proc., vol. 56, pp. 1927-1934, 2022. https://doi.org/10.1016/j.matpr.2021.11.199
[14]A. Albladi, M. Islam, and C. Seals, "Sentiment analysis of Twitter data using NLP models: A comprehensive review," IEEE Access, 2025. doi: 10.1109/ACCESS.2025.3541494
[15]R. Jain, A. Kumar, A. Nayyar, K. Dewan, R. Garg, S. Raman, and S. Ganguly, "Explaining sentiment analysis results on social media texts through visualization," Multimedia Tools Appl., vol. 82, no. 15, pp. 22613-22629, 2023. https://doi.org/10.1007/s11042-023-14432-y
[16]S. Sharma, R. Aggarwal, and M. Kumar, "Mining Twitter for insights into ChatGPT sentiment: A machine learning approach," in Proc. 2023 Int. Conf. on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), pp. 1-6, 2023. doi: 10.1109/ICDCECE57866.2023.10150620
[17]S. Bengesi, T. Oladunni, R. Olusegun, and H. Audu, "A machine learning-sentiment analysis on monkeypox outbreak: An extensive dataset to show the polarity of public opinion from Twitter tweets," IEEE Access, vol. 11, pp. 11811-11826, 2023.doi: 10.1109/ACCESS.2023.3242290
[18]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, pp. 14778-14803, 2023. doi: 10.1109/ACCESS.2023.3242234
[19]P. Gaur, S. Vashistha, and P. Jha, "Twitter sentiment analysis using Naive Bayes-based machine learning technique," in Sentiment Analysis and Deep Learning: Proc. ICSADL 2022, Singapore: Springer Nature, 2023, pp. 367-376.[20] A. Alslaity and R. Orji, "Machine learning techniques for emotion detection and sentiment analysis: Current state, challenges, and future directions," Behav. Inf. Technol., vol. 43, no. 1, pp. 139-164, 2024. https://doi.org/10.1007/978-981-19-5443-6_27
[20]F. Benrouba and R. Boudour, "Emotional sentiment analysis of social media content for mental health safety," Social Network Anal. Min., vol. 13, no. 1, p. 17, 2023. DOI: 10.21203/rs.3.rs-2170906/v1
[21]K. Naithani and Y. P. Raiwani, "Realization of natural language processing and machine learning approaches for text-based sentiment analysis," Expert Syst., vol. 40, no. 5, p. e13114, 2023. https://doi.org/10.1111/exsy.13114
[22]Wu, Yichao, et al. "Research on the Application of Deep Learning-based BERT Model in Sentiment Analysis." ArXiv, 2024. https://arxiv.org/abs/2403.08217
[23]Jia, Keliang, et al. "Text Sentiment Analysis Based on BERT-CBLBGA." Computers and Electrical Engineering, vol. 112, 2023, p. 109019. https://doi.org/10.1016/j.compeleceng.2023.109019
[24]JP797498E, Twitter Entity Sentiment Analysis Dataset, Kaggle, [Online]. Available: https://www.kaggle.com/datasets/jp797498e/twitter-entity-sentiment-analysis. Accessed: Jan. 30, 2026.