Browsing by Author "Faiza MERAH Nawel FALEK"

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    Swarm Intelligence for Sentiment Analysis from Comments
    (2024) Faiza MERAH Nawel FALEK
    Sentiment analysis is an NLP technique used to identify the emotional tone of a piece of text. This method helps discern the sentiment conveyed in texts like reviews, comments, social media posts, or any other data involving human language. To understand and categorise the sentiment conveyed in textual data, machine learning offers methods and tools necessary to create models capable of this. ML techniques are numerous, for this work Support Vector Machine(svm) is used; but this algorithm relies on a set of hyper-parameters which greatly influence its performance. Therefore, this study takes hyper-parameter tuning as an optimization problem and employs a swarm-based optimization technique to enhance the performance of SVM in sentiment analysis by using the recently introduced Termite Alate Optimization Algorithm. The performance of proposed approach is evaluated using five metrics: accuracy, precision, recall, F1-measure and computation time on five well known datasets in the field of sentiment analysis and compared to another famous swarm-based algorithm: Particle Swarm Optimization. The experimental results show significant improvement in the SVM performance with optimized hyper-parameters by the proposed approach compared to SVM with default hyper-parameters in all datasets. Remarkably, TAOA demonstrated faster tuning compared to PSO, completing its process with notable efficiency.