IntegrativeSentiment.io (iS.io) A Web-based Interactive Tool for Modeling Human Feedback on Big Social Media Data 
Publication LinkSoftware Link
Team: Mehdi Ashayeri, PhD (PI)Narjes Abbasabadi, PhD (co-PI)Soroush Piri (Graduate Assistant)
SYNOPSISiS.io is a web-based tool that provides an interactive and user-friendly platform for modeling human feedback based on real-world big textual data like Twitter data. It leverages the power of Natural Language Processing (NLP) and Emotional Intelligence frameworks to perform real-time sentiment analysis on the data. With iS.io, data acquisition, cleaning, and tokenizing processes are automated, allowing even non-skilled users to perform NLP analyses. Moreover, iS.io implements graph theories and geospatial mapping tools that enable users to visualize the results in a clear and insightful manner. By filtering the geo-location and time durations, users can tailor their analyses to their desired spatiotemporal horizons and granularities, making the tool highly adaptable and versatile for any research purposes.Built based on the R programming language, iS.io uses the R engine embedded in the URBiiLAB's web-portal, providing users with a seamless experience without the need to directly work with R interfaces such as RStudio software. As such, no coding skills are required to develop and execute the model. iS.io can be especially beneficial for designers, planners, and researchers, among other user groups, who want to explore human feedback on real-world subjects comprehensively and effectively.It is worth mentioning that iS.io is part of a larger project that aims to explore human feedback within the built environment for sustainability assessments of buildings, communities, and cities. By providing accurate and insightful analyses of human feedback data, iS.io can contribute to making the built environment more sustainable, functional, and responsive to people's needs.


Workshop 2:
IntegrativeSentiment.io (iS.io) (ver. 1.0)A Web-based Interactive Tool for Modeling Human Feedback on Big Data
Workshop Instructor: Mehdi Ashayeri, Southern Illinois UniversityWorkshop Assistant: Soroush Piri, Graduate Student, Southern Illinois University
Duration: 3 hours Held between 1 p.m. to 4 p.m. PT (July 17, 2022)Maximum capacity: 35 attendees
Link to Workshop: http://simaud.org/2022/call_for_workshops.phpLink to Workshop Poster: http://simaud.org/2022/workshops/SimAUDw02-ModelingHumanFeedback.pdf