TWEET SENTIMENT ANALYSISON GREENSPACES

Denaro, Lino Garda
Sujana, Yudianto
Fatihul Ilmy, Hafsah

Journal of Marine-Earth Science and Technology
Vol. 2 No. 2 (2021): September

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.