2019
JMLR
JMLR 2019
AffectiveTweets: a Weka Package for Analyzing Affect in Tweets
Abstract
AffectiveTweets is a set of programs for analyzing emotion and sentiment of social media messages such as tweets. It is implemented as a package for the Weka machine learning workbench and provides methods for calculating state-of-the-art affect analysis features from tweets that can be fed into machine learning algorithms implemented in Weka. It also implements methods for building affective lexicons and distant supervision methods for training affective models from unlabeled tweets. The package was used by several teams in the shared tasks: EmoInt 2017 and Affect in Tweets SemEval 2018 Task 1. [abs] [ pdf ][ bib ] [ code ] © JMLR 2019. (edit, beta)
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Interdisciplinary Bridge
— Computer Science and Machine Learning
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Keyword Pioneer
— lexicon building
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Hot Topic Early Bird
— affective computing
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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