We use novel Natural Language Processing
to extract Sentiment and Topics
from Social Media
What is the impact of climate events, such as temperature increases and environmental disasters, on subjective well-being and happiness? This project aims at addressing this issue by posing the following research questions:
Our sentiment index relies on years of geolocated social media posts from Twitter and Weibo. Social media data is collected by the Harvard Center for Geographic Analysis Geotweet Archive and by the MIT Sustainable Urbanization Lab. Each post is encoded into vectorial representations using several Natural Language Processing (NLP) techniques, including dictionary-based methods (LIWC, Emoji dictionaries) and embedding-based algorithms (BERT). Pre-trained machine learning algorithms use these representations to impute the Twitter data’s sentiment and main topics, every day and everywhere.
Siqi Zheng
Faculty Director and PI
Jianghao Wang
Research Scientist
Nicolas
Guetta-Jeanrenaud
Graduate Researcher
Ajkel Mino
Graduate Researcher
Yichun Fan
Ph.D. Student and Graduate Researcher
Yuchen Chai
Graduate Researcher
Juan Palacios
Head of Research
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