Abstract
Sentiment analysis, an extensively explored area in the realm of natural language processing, holds the utmost importance for a wide range of applications. This research addresses key limitations in sentiment analysis studies, encompassing contextual ambiguities, difficulties in sarcasm coverage, and a deficiency of labeled data across varied domains. To address these limitations, this study introduces LexiSNTAGMM, an innovative framework designed specifically for sentiment analysis in social networks. By seamlessly integrating a dictionary-based approach with an unsupervised machine-learning model, LexiSNTAGMM aspires to improve sentiment analysis by addressing these identified limitations. The framework's robust performance is demonstrated through a comprehensive evaluation across diverse datasets, including STStest, SentiStrength, HCR, Sanders, OMD, and Strict OMD, resulting in exemplary F1 scores of 84.12, 76.90, 73.83, 86.12, 81.06, and 86.01, respectively. These compelling outcomes firmly establish LexiSNTAGMM's superiority over conventional supervised methods, positioning it favorably in comparison to contemporary approaches.
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Bashiri, H., Naderi, H. LexiSNTAGMM: an unsupervised framework for sentiment classification in data from distinct domains, synergistically integrating dictionary-based and machine learning approaches. Soc. Netw. Anal. Min. 14, 102 (2024). https://doi.org/10.1007/s13278-024-01268-z
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DOI: https://doi.org/10.1007/s13278-024-01268-z