GIS-based fuzzy sentiment analysis framework to classify urban elements according to the orientations of citizens and tourists expressed in social networks

[1]  Naimat Ullah Khan,et al.  Analyzing the Spatiotemporal Patterns in Green Spaces for Urban Studies Using Location-Based Social Media Data , 2019, ISPRS Int. J. Geo Inf..

[2]  Salvatore Sessa,et al.  A lightweight clustering–based approach to discover different emotional shades from social message streams , 2019, Int. J. Intell. Syst..

[3]  Uzay Kaymak,et al.  Fuzzy clustering with volume prototypes and adaptive cluster merging , 2002, IEEE Trans. Fuzzy Syst..

[4]  Lan Mu,et al.  GIS analysis of depression among Twitter users , 2015 .

[5]  Salvatore Sessa,et al.  The extended fuzzy C-means algorithm for hotspots in spatio-temporal GIS , 2011, Expert Syst. Appl..

[6]  Sumbal Riaz,et al.  Opinion mining on large scale data using sentiment analysis and k-means clustering , 2019, Cluster Computing.

[7]  Erik Cambria,et al.  Affective Computing and Sentiment Analysis , 2016, IEEE Intelligent Systems.

[8]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[9]  Weitong Chen,et al.  A survey of sentiment analysis in social media , 2018, Knowledge and Information Systems.

[10]  Thomas Blaschke,et al.  Beyond Spatial Proximity - Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data , 2018, ISPRS Int. J. Geo Inf..

[11]  Joseph G. Allen,et al.  Using Twitter to Better Understand the Spatiotemporal Patterns of Public Sentiment: A Case Study in Massachusetts, USA , 2018, International journal of environmental research and public health.

[12]  R. Plutchik The Nature of Emotions , 2001 .