SentimentLP: unveiling advanced sentiment analysis through Leptotila optimization-based gradient boosting machines
Anitha Merlin Durairaj John Louis, Vimal Kumar Dhanasekaran
Abstract
Sentiment analysis is pivotal in extracting insights from textual data, enabling organizations to understand customer opinions, market trends, and brand perception. This study introduces a novel approach, SentimentLP, which integrates Leptotila optimization (LPO) with gradient boosting machines (GBM) for sentiment analysis tasks. The proposed framework leverages LPO’s dynamic optimization capabilities to enhance GBM models’ performance in sentiment classification. Through iterative refinement and adaptive learning, SentimentLP optimizes feature extraction, model training, and ensemble learning processes, improving sentiment analysis accuracy and efficiency. Results from various evaluation metrics, including precision, recall, classification accuracy, and F-measure, demonstrate the effectiveness of SentimentLP in accurately capturing sentiment expressions in text data. Additionally, the fusion of LPO with GBM ensures scalability, adaptability, and interpretability of sentiment analysis models, making SentimentLP a valuable tool for extracting actionable insights from textual data across diverse domains and applications.
Keywords
Gradient boosting; Lepotila optimization; Neural network; Online shopping; Sentiment
DOI:
https://doi.org/10.11591/eei.v14i2.8959
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Bulletin of EEI Stats
Bulletin of Electrical Engineering and Informatics (BEEI) ISSN: 2089-3191, e-ISSN: 2302-9285 This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU) .