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- W2920087251 abstract "The advent of media transition highlights the importance of user-generated content on social media. Amongst the methods of analysis of user-generated content, sentiment analysis is widely used. Nevertheless, few studies use sentiment analysis to investigate user-generated content on Instagram in the context of public libraries. Therefore, this study aims to fill this research gap by conducting sentiment analysis of two million captions on Instagram. Supervised machine learning algorithms were employed to create the classifier. Three opinion polarities and six emotions were ultimately identified via these captions. These polarities provide new insights for understanding readers, thus helping libraries to deliver better services." @default.
- W2920087251 created "2019-03-11" @default.
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- W2920087251 date "2018-12-08" @default.
- W2920087251 modified "2023-09-24" @default.
- W2920087251 title "Understanding Readers" @default.
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- W2920087251 doi "https://doi.org/10.1145/3297156.3297270" @default.
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