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• W3138515901 abstract "This paper presents a sentiment analysis solution on tweets using Recurrent Neural Networks (RNNs). The method is can classifying tweets with an 80.74% accuracy rate, considering a binary task, after experimenting with 20 different design approaches. The solution integrates an attention mechanism aiming to enhance the network, with a two-way localization system: at memory cell level and at network level. We present an in-depth literature review for Twitter sentiment analysis and the building blocks that grounded the design decisions of our solution, employed as a core classification component within a sentiment indicator of the SynergyCrowds platform." @default.
• W3138515901 created "2021-03-29" @default.
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• W3138515901 date "2021-03-24" @default.
• W3138515901 modified "2023-10-16" @default.
• W3138515901 title "Building a Twitter Sentiment Analysis System with Recurrent Neural Networks" @default.
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• W3138515901 doi "https://doi.org/10.3390/s21072266" @default.
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