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- W2912491947 abstract "Researchers on social-media understandably assert that the contributions social media has made on various sectors is massive. Business development managers today have directed a huge amount of effort in strategizing efficient collaboration with both customers and other organizations using social-media. Despite the visible impact social media has made, a lot of digitally shared information is yet to be revealed. Gradually twitter has become the main hub for many Information system researchers, because tweets can freely be accessible in real-time by any one. Motivated by earlier studies where IS researchers addressed big-data analysis and management by employing content analysis techniques, this paper proposes a novel approach to perform unsupervised classification of the tweets into different labels. It introduces a unique algorithm that uses semantic similarity between texts, Term-frequency and a determinant threshold to perform content analysis. The goal of this approach is to extract relevant features from a tweet thus reducing dimension and preparing training datasets that would be used to build classifiers." @default.
- W2912491947 created "2019-02-21" @default.
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- W2912491947 date "2018-01-01" @default.
- W2912491947 modified "2023-09-23" @default.
- W2912491947 title "Text annotation using textual semantic similarity and term-frequency (Twitter)" @default.
- W2912491947 hasPublicationYear "2018" @default.
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