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.
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This project is partially supported by Artificial Intelligence Research Unit (AiRU).
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Lim, W.L., Ho, C.C., Ting, CY. (2020). Tweet sentiment analysis using deep learning with nearby locations as features. In: Alfred, R., Lim, Y., Haviluddin, H., On, C. (eds) Computational Science and Technology. Lecture Notes in Electrical Engineering, vol 603. Springer, Singapore. https://doi.org/10.1007/978-981-15-0058-9_28
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DOI: https://doi.org/10.1007/978-981-15-0058-9_28
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