Tweet sentiment analysis using deep learning with nearby locations as features

Citation

Lim, Wei Lun and Ho, Chiung Ching and Ting, Choo Yee (2020) Tweet sentiment analysis using deep learning with nearby locations as features. In: Computational Science and Technology. Lecture Notes in Electrical Engineering (Computational Science and Technology), 603 . Springer Verlag, pp. 291-299. ISBN 9789811500572

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Abstract

Twitter classification using deep learning have shown a great deal of promise in recent times. Many works had been performed on twitter sentiment analysis but there has not been much work done investigating the effects of location on twitter sentiment analysis. In this study, we concatenated text and location features as a feature vector for twitter sentiment analysis using a deep learning classification approach specifically Convolutional Neural Network (CNN). The achieved results show that using location as a feature alongside text has increased the sentiment analysis accuracy.

Item Type: Book Section
Uncontrolled Keywords: Neural networks (Computer science), sentiment analysis, location analysis, natural language understanding, deep learning, convolutional neural network
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 21 Dec 2020 05:36
Last Modified: 21 Dec 2020 05:36
URII: http://shdl.mmu.edu.my/id/eprint/7950

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