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- W2912707329 abstract "Sentiment analysis is a growing research area that analyzes people’s opinions towards a specific target using posts shared in social media. However, spammers can inject false opinions to change sentiment-oriented decisions, e.g. low quality products or policies can be promoted or advocated over others. Therefore, identifying and removing spam posts in social media is a crucial data cleaning operation for text mining tasks including sentiment analysis. An inherent problem related to spam detection is the imbalanced-class problem. In this paper, we explore the impact of imbalance ratio on the performance of Twitter spam detection using multiple approaches of single and ensemble classifiers. Besides ensemble-based learning (Bagging and Random forest), we apply the SMOTE oversampling technique to improve detection performance especially for classifiers sensitive to imbalanced datasets." @default.
- W2912707329 created "2019-02-21" @default.
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- W2912707329 date "2019-01-01" @default.
- W2912707329 modified "2023-09-25" @default.
- W2912707329 title "Statistical Comparison of Opinion Spam Detectors in Social Media with Imbalanced Datasets" @default.
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- W2912707329 doi "https://doi.org/10.1007/978-981-13-5826-5_12" @default.
- W2912707329 hasPublicationYear "2019" @default.
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