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2019 ACL ACL 2019

Predicting Human Activities from User-Generated Content

Abstract

AbstractThe activities we do are linked to our interests, personality, political preferences, and decisions we make about the future. In this paper, we explore the task of predicting human activities from user-generated content. We collect a dataset containing instances of social media users writing about a range of everyday activities. We then use a state-of-the-art sentence embedding framework tailored to recognize the semantics of human activities and perform an automatic clustering of these activities. We train a neural network model to make predictions about which clusters contain activities that were performed by a given user based on the text of their previous posts and self-description. Additionally, we explore the degree to which incorporating inferred user traits into our model helps with this prediction task.

🌉 Interdisciplinary Bridge — Data Science & Analytics and Deep Learning and Machine Learning
🧭 Keyword Pioneer — automatic clustering
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Steven Wilson , Rada Mihalcea

Topics

Machine Learning > Core Methods > Clustering Machine Learning > Learning Types > Unsupervised Learning Deep Learning > Learning Types > Representation Learning Data Science & Analytics > Applications > Social Media Analysis

Keywords

neural network model sentence embedding user-generated content activity prediction neural network automatic clustering human activity prediction
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