ALGO-DeAM: An improved Deep Ensemble Model for Twitter Sentimental Analysis using Ateles Leading Gorilla Optimizer

Abstract

In recent days, the evolving growth of social-media applications and their reviews have given rise to Sentimental analysis (SA) to analyze the attitudes, feelings, and views of the users. However, the traditional sentimental analysis mechanism possessed limitations in understanding the context of the text, generalization, interpretability, inaccurate analysis, and dialectal variation. Therefore, to address these aforementioned issues, the Ateles Leading Gorilla Optimizer enabled Deep Ensemble Activation Model (ALGO-DeAM) is proposed in this research. Specifically, the Ateles Leading Gorilla Optimization (ALGO) tunes the hyperparameters and selects the optimal features of the ALGO-DeAM model, which in turn accelerates the training process and minimizes the computation complexity. In real-time SA applications, this hybrid optimization approach offers enhanced accuracy, resilience, and efficiency, making it especially useful for processing high-dimensional and dynamic sentiment data. The DeAM takes advantage of various learning patterns and improves performance by capturing multiple aspects of the input. The proposed ALGO-DeAM attains higher performance with the metrics of accuracy, sensitivity, and specificity, as 98.09%, 96.91%, and 98.69% using the Sentiment140 dataset, 98.15%, 98.00%, and 98.43% with Twitter sentiment dataset, and 96.59%, 97.19%, and 97.15% with Emotion dataset respectively.

Keywords

Sentimental analysis / Twitter data / natural language processing / online reviews / deep learning

References

[1] Abdollahzadeh B, Soleimanian Gharehchopogh F, Mirjalili S. Artificial gorilla troops optimizer: A new natureinspired metaheuristic algorithm for global optimization problems. International Journal of Intelligent Systems, 2021, 36(10): 5887-5958

[2] Agrawal A, Garg D, Popli D, Banerjee A, Raj A, Dikshit I (2023). A review of spider monkey optimization: Modification and its biomedical application. International Journal on Interactive Design and Manufacturing (JJIDeM): 1–22.

[3] Ala A, Simic V, Pamucar D, Tirkolaee E B. Appointment scheduling problem under fairness policy in healthcare services: Fuzzy ant lion optimizer. Expert Systems with Applications, 2022, 207: 117949

[4] Aliman G, Nivera TFS, Olazo J C A, Ramos D J P, Sanchez C D B, Amado T M, Valenzuela I C. Sentiment analysis using logistic regression. Journal of Computational Innovations and Engineering Applications, 2022, 7(1): 35-40

[5] Aslan S, Kzloluk S, Sert E. TSA-CNN-AOA: Twitter sentiment analysis using CNN optimized via arithmetic optimization algorithm. Neural Computing and Applications, 2023, 35(14): 10311-10328

[6] Assiroj P, Kurnia A, Alam S. The performance of Naïve Bayes, support vector machine, and logistic regression on Indonesia immigration sentiment analysis. Bulletin of Electrical Engineering and Informatics, 2023, 12(6): 3843-3852

[7] Basiri M E, Nemati S, Abdar M, Cambria E, Acharya U R. ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis. Future Generation Computer Systems, 2021, 115: 279-294

[8] Catelli R, Pelosi S, Esposito M. Lexicon-based vs. Bert-based sentiment analysis: A comparative study in Italian. Electronics, 2022, 11(3): 374

[9] Dang N C, Moreno-García M N, De la Prieta F. Sentiment analysis based on deep learning: A comparative study. Electronics, 2020, 9(3): 483

[10] Daniel D A J D (2023). A hybrid sentiment analysis approach using black widow optimization based feature selection. Journal of Engineering Research 11(2A).

[11] Das R, Singh T D. Multimodal sentiment analysis: A survey of methods, trends, and challenges. ACM Computing Surveys, 2023, 55(13s): 1-38

[12] Elgeldawi E, Sayed A, Galal A R, Zaki A M. Hyperparameter tuning for machine learning algorithms used for Arabic sentiment analysis. Informatics, 2021, 8(4): 79

[13] El-Shorbagy M A, Bouaouda A, Nabwey H A, Abualigah L, Hashim F A. Bald eagle search algorithm: A comprehensive review with its variants and applications. Systems Science & Control Engineering, 2024, 12(1): 2385310

[14] Gadri S, Mehieddine S O, Herizi K, Chabira S. An efficient system to predict customers’ satisfaction on touristic services using ML and DL approaches. 2021 22nd International Arab Conference on Information Technology (ACIT), 20211-8

[15] Gandhi A, Adhvaryu K, Poria S, Cambria E, Hussain A. Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions. Information Fusion, 2023, 91: 424-444

[16] Ganesh V, Kamarasan M. Parameter tuned bidirectional long short term memory based emotion with intensity sentiment classification model using Twitter data. 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), 20201-6

[17] Georgiou E, Katsouros V, Avrithis Y, Potamianos A (2025). DeepMLF: Multimodal language model with learnable tokens for deep fusion in sentiment analysis. arXiv preprint: arXiv 2504.11082.

[18] Jacob N, Viswanatham V M. Sentiment analysis using improved atom search optimizer with a simulated annealing and ReLU based gated recurrent unit. IEEE Access, 2024, 12: 38944-38956

[19] Jahanbin K, Chahooki M A Z. A hybrid deep implicit neural model for sentiment analysis via transfer learning. IEEE Access, 2024, 12: 131468-131486

[20] Jim J R, Talukder M A R, Malakar P, Kabir M M, Nur K, Mridha M F. Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review. Natural Language Processing Journal, 2024, 6: 100059

[21] Khan T A, Sadiq R, Shahid Z, Alam M M, Su’ud M B. Sentimental analysis using support vector machine and random forest. Journal of Informatics and Web Engineering, 2024, 3(1): 67-75

[22] Kumar S, De K, Roy P P. Movie recommendation system using sentiment analysis from microblogging data. IEEE Transactions on Computational Social Systems, 2020, 7(4): 915-923

[23] Kusumaningrum R, Arafifin A H, Khoerunnisa S F, Sasongko P S, Wirawan P W, Syafrudin M. Hyperparameter optimization for convolutional neural network-based sentiment analysis. Journal of Advanced Research in Applied Sciences and Engineering Technology, 2025, 53(1): 44-56

[24] Loh N K N, Lee C P, Ong T S, Lim K M. MPNet-GRUs: Sentiment analysis with masked and permuted pre-training for language understanding and gated recurrent units. IEEE Access, 2024, 12: 74069-74080

[25] Maitama J Z, Idris N, Abdi A, Shuib L, Fauzi R. A systematic review on implicit and explicit aspect extraction in sentiment analysis. IEEE Access, 2020, 8: 194166-194191

[26] Myint PYW, Lo SL, Zhang Y. Unveiling the dynamics of crisis events: Sentiment and emotion analysis via multi-task learning with attention mechanism and subject-based intent prediction. Information Processing & Management, 2024, 61(4): 103695

[27] Naresh Kumar K E, Uma V. Intelligent sentinet-based lexicon for context-aware sentiment analysis: Optimized neural network for sentiment classification on socialmedia. The Journal of Supercomputing, 2021, 77(11): 12801-12825

[28] Nazir A, Rao Y, Wu L, Sun L. Issues and challenges of aspect-based sentiment analysis: A comprehensive survey. IEEE Transactions on Affective Computing, 2020, 13(2): 845-863

[29] Pandian A P. Performance evaluation and comparison using deep learning techniques in sentiment analysis. Journal of Soft Computing Paradigm (JSCP), 2021, 3(2): 123-134

[30] Parveen N, Chakrabarti P, Hung B T, Shaik A. Twitter sentiment analysis using hybrid gated attention recurrent network. Journal of Big Data, 2023, 10(1): 50

[31] Pontes E L, Benjannet M. Contextual sentence analysis for the sentiment prediction on financial data. 2021 IEEE International Conference on Big Data (Big Data), 20214570-4577

[32] Shobana J, Murali M. Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis. Journal of Intelligent & Fuzzy Systems, 2021, 40(6): 10703-10719

[33] Sun H, Wang H, Liu J, Chen Y W, Lin L. CubeMLP: An MLP-based model for multimodal sentiment analysis and depression estimation. Proceedings of the 30th ACM International Conference on Multimedia, 20223722-3729

[34] Tabany M, Gueffal M. Sentiment analysis and fake amazon reviews classification using SVM supervised machine learning model. Journal of Advances in Information Technology, 2024, 15(1): 49-58

[35] Talaat A S. Sentiment analysis classification system using hybrid BERT models. Journal of Big Data, 2023, 10(1): 110

[36] Tan K L, Lee C P, Lim K M. A survey of sentiment analysis: Approaches, datasets, and future research. Applied Sciences, 2023, 13(7): 4550

[37] Tiwari D, Nagpal B, Bhati B S, Gupta M, Suanpang P, Butdisuwan S, Nanthaamornphong A. SPSO-EFVM: A particle swarm optimization-based ensemble fusion voting model for sentence-level sentiment analysis. IEEE Access, 2024, 12: 23707-23724

[38] Umarani V, Julian A, Deepa J (2021). Sentiment analysis using various machine learning and deep learning techniques. Journal of the Nigerian Society of Physical Sciences: 385–394.

[39] Wang Y, Huang G, Li J, Li H, Zhou Y, Jiang H. Refined global word embeddings based on sentiment concept for sentiment analysis. IEEE Access, 2021, 9: 37075-37085

[40] Wang Z, Ala A, Liu Z, Cui W, Ding H, Jin G, Lu X (2024). A hybrid equilibrium optimizer based on moth flame optimization algorithm to solve global optimization problems. Journal of Artificial Intelligence and Soft Computing Research 14.

[41] Wang Z, Ho S B, Cambria E. Multi-level fine-scaled sentiment sensing with ambivalence handling. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2020, 28(4): 683-697

[42] Wankhade M, Rao A C, Kulkarni C. A survey on sentimental analysis methods, applications, and challenges. Artificial Intelligence Review, 2022, 55(7): 5731-5780

[43] Yadav A, Vishwakarma D K. Sentiment analysis using deep learning architectures: A review. Artificial Intelligence Review, 2020, 53(6): 4335-4385

[44] Yang L, Li Y, Wang J, Sherratt R S. Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE Access, 2020, 8: 23522-23530

[45] Zhang X, Mao R, He K, Cambria E. Neuro-symbolic sentiment analysis with dynamic word sense disambiguation. Findings of the Association for Computational Linguistics: EMNLP 2023, 20238772-8783

[46] Zhang Y, Song J, Sciacca A, Chan J, Qi X. Novel sentiment lexica derived from user generating content by Chinese tourists in Pacific Islands. Sustainability, 2022, 14(23): 15833

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