TWEET SENTIMENT ANALYSISON GREENSPACES

Authors

  • Lino Garda Denaro Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
  • Yudianto Sujana Department of Geomatics, National Cheng Kung University, Tainan, Taiwan
  • Hafsah Fatihul Ilmy Department of Geomatics, National Cheng Kung University, Tainan, Taiwan

DOI:

https://doi.org/10.12962/j27745449.v2i2.105

Keywords:

sentiment analysis, text mining, twitter data, Support Vector Machine, machine learning

Abstract

Twitter has become one of the most significant resources for text mining. Twitter can provide information    about human activities, mobility, and emotional patterns along with location data. Many types of text research can be made with these data, one of which is sentiment analysis. This study evaluates the potential of deriving emotional responses of individuals from tweets while they experience and interact with urban green space. A machine learning model using Support Vector Machine (SVM) and corpus from over 2000 movie reviews has been made. This model is used to classify incoming tweets into positive and negative sentiments. Then the web-based recommender system has been built to provide suggestions for green spaces based on users' preferred activities.

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Published

2021-11-09

How to Cite

Denaro, L. G., Sujana, Y., & Fatihul Ilmy, H. (2021). TWEET SENTIMENT ANALYSISON GREENSPACES. Journal of Marine-Earth Science and Technology, 2(2), 51–54. https://doi.org/10.12962/j27745449.v2i2.105

Issue

Vol. 2 No. 2 (2021): September

Section

Articles