Lecture

Twitter Mood Analysis

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In course

DH-500: Computational Social Media

The course integrates concepts from media studies, machine learning, multimedia, and network science to characterize social practices and analyze content in platforms like Twitter/X, Instagram, YouTube, and TikTok. Students will learn computational methods ...

Description

This lecture delves into understanding mood expressed on Twitter, where positive affect (PA) and negative affect (NA) are independent dimensions. The goal is to study variations in PA & NA over time, day of week, and world region using longitudinal Twitter data. The lecture covers the extraction of PA and NA, the use of LIWC2015 for text analysis, and visualizing Twitter mood. It also explores hourly changes in individual affect in different regions and emphasizes the importance of considering biased data in large-scale human behavior analysis.

Instructor

Daniel Gatica-Perez

Expertise Social computing, social media, ubiquitous computing, crowdsourcing, machine learning. Current work My research integrates theories and methods from ubiquitous computing, social media, machine learning, and social sciences to understand human behavior in daily life and to create applications for social good.My current research interests include:* Mobile crowdsourcing and social media analytics for social good.* Crowdsourcing for cities.* Social video analytics, with focus on conversational behavio

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