Adaptive Ensemble Learning Model Using Evolutionary Computing for Sentiment Analysis

Jun Ikram^*
*Correspondence: Jun Ikram, Department of Aerospace, University of Sydney, Sydney, Australia, Email:

Introduction

Adaptive ensemble learning models have gained significant attention in sentiment analysis due to their ability to improve classification accuracy and adaptability to varying datasets. Sentiment analysis, a subfield of Natural Language Processing (NLP), involves determining the sentiment polarity of a given text, whether positive, negative, or neutral. Traditional machine learning models often struggle with sentiment classification due to data complexity, subjectivity, and evolving linguistic patterns. To address these challenges, an adaptive ensemble learning model utilizing evolutionary computing is proposed, integrating multiple classifiers and optimizing their combination dynamically. Ensemble learning techniques involve combining multiple weak classifiers to create a more robust predictive model. Popular ensemble methods include bagging, boosting, and stacking. Each of these approaches has advantages, but they may not always adapt efficiently to diverse datasets. Evolutionary computing, inspired by natural selection, provides a solution by dynamically optimizing the ensemble structure and classifier weights based on dataset characteristics. The combination of these two methodologies results in a powerful adaptive sentiment analysis model.

Description

The foundation of the proposed model lies in evolutionary computing algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). These techniques optimize the selection and weighting of base classifiers in the ensemble. Traditional ensemble learning methods rely on predefined structures that may not generalize well to all datasets. Evolutionary computing enhances adaptability by iteratively refining the ensemble model based on performance metrics such as accuracy, precision, recall, and F1- score. In constructing the ensemble, a diverse set of base classifiers, including Support Vector Machines (SVM), Random Forest (RF), and deep learning models such as Bidirectional Long Short-Term Memory (BiLSTM) networks, are employed. Each classifier contributes to sentiment classification, leveraging different strengths. For instance, SVM excels at handling high-dimensional text data, RF provides interpretability and robustness, and BiLSTM captures longrange dependencies in textual data. A critical aspect of this approach is feature engineering, which involves preprocessing text data before feeding it into the classifiers. Standard preprocessing steps include tokenization, stopword removal, stemming, and word embedding techniques such as Word2Vec or BERT embeddings. These methods ensure that the text data is transformed into numerical representations suitable for model training [1-3].

To construct the evolutionary ensemble model, an initial population of classifier combinations is generated. Each individual in this population represents a unique ensemble configuration, including different classifiers and their corresponding weight distributions. The fitness function evaluates each individual based on sentiment classification performance on a validation dataset. Evolutionary operations such as selection, crossover, and mutation are applied iteratively to improve the ensemble's performance. One advantage of evolutionary ensemble learning is its ability to dynamically adapt to dataset shifts. In sentiment analysis, linguistic trends and vocabulary usage change over time. Traditional static models may become outdated and fail to generalize to new data. The adaptive nature of evolutionary computing enables the ensemble model to continuously evolve, selecting classifiers that best capture emerging sentiment patterns. To validate the effectiveness of the proposed model, experiments are conducted on benchmark sentiment analysis datasets such as IMDB movie reviews, Twitter sentiment datasets, and product review datasets. Performance comparisons with traditional ensemble methods and standalone classifiers demonstrate the superiority of the adaptive evolutionary computing approach. Evaluation metrics such as accuracy, F1-score, and confusion matrices highlight improvements in classification robustness and generalizability [4,5].

Conclusion

Hyperparameter tuning plays a crucial role in optimizing both the individual classifiers and the evolutionary algorithm parameters. The number of generations, mutation rate, and selection strategy in the evolutionary process directly influence the model's performance. Automated hyperparameter tuning using techniques like Bayesian optimization further enhances efficiency. Beyond benchmark datasets, real-world applications of this model extend to sentiment monitoring in social media, customer feedback analysis, and financial market predictions based on sentiment trends. The model's adaptability makes it suitable for diverse domains where sentiment analysis plays a critical role in decision-making. One limitation of the proposed approach is computational complexity. Evolutionary computing requires multiple iterations of classifier evaluations, which can be resource-intensive. However, parallel computing and cloud-based solutions can mitigate these challenges by distributing computational workloads. Future research directions include incorporating reinforcement learning techniques to further enhance adaptability, exploring transformer-based classifiers for improved contextual understanding, and integrating domain-specific sentiment lexicons to enhance interpretability. In conclusion, the adaptive ensemble learning model utilizing evolutionary computing presents a novel and effective approach for sentiment analysis. By leveraging diverse classifiers, optimizing ensemble configurations dynamically, and adapting to dataset shifts, this model overcomes challenges faced by traditional sentiment classification methods. Experimental results validate its effectiveness, making it a promising solution for real-world sentiment analysis applications.

Acknowledgment

None.

Conflict of Interest

None.

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