Publication: Enhanced Gray Wolf Algorithm Based Feature Selection For Sentiment Analysis
Date
2025-08
Authors
Alqablan, Tamara Amjad Abdelkarim
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Abstract
People access social media platforms through a range of IoT devices, including smartphones, tablets, and wearable devices, which lead to the generation of a significant quantity of data. The high dimensional of data is considered a major challenge in analyzing and extracting knowledge from big data. Feature selection is a technique concerned with selecting only the most relevant features to improve performance. In the context of wrapper-based feature selection approaches, the algorithm is often stuck in a poor solution (feature subset) due to its inability to efficiently explore the search space. To tackle this problem, first, an enhanced wrapper-based approach that combines the BGWO algorithm with the AβHC local search algorithm (Aβ-BGWO) was proposed to enhance the BGWO exploitation capabilities and improve its performance in terms of local search ability. Second, enhance the Aβ-BGWO by integrating it with BHHO (Aβ-BGWHHO) to improve the global convergence of A𝛽-BGWO, enhance its exploration capabilities, and fine-tune between exploitation and exploration. Third, due to the nature of the sentiment analysis datasets, filter methods can effectively identify and eliminate noise features, thereby enhancing the quality of feature subsets evaluated by the wrapper approach.
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Keywords
Enhanced Gray Wolf Algorithm , Feature Selection For Sentiment Analysis