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• W2023239346 abstract "This study examines several approaches to sentiment classification in the Dark Web Forum Portal, and opportunities to transfer classifiers and text features across multiple forums to improve scalability and performance. Although sentiment classifiers typically perform poorly when transferred across domains, experimentation reveals the devised approaches offer performance equivalent to the traditional forum-specific approach in classification in an unknown domain. Furthermore, incorporating the text features identified as significant indicators of sentiment in other forums can greatly improve the classification accuracy of the traditional forum-specific approach." @default.
• W2023239346 created "2016-06-24" @default.
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• W2023239346 date "2012-06-01" @default.
• W2023239346 modified "2023-09-24" @default.
• W2023239346 title "Scalable sentiment classification across multiple Dark Web Forums" @default.
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• W2023239346 doi "https://doi.org/10.1109/isi.2012.6284095" @default.
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