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SemEval-2018 Task 1: Affect in Tweets
TLDR
This work presents the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet, with a focus on the techniques and resources that are particularly useful.
WASSA-2017 Shared Task on Emotion Intensity
We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities
Emotion Intensities in Tweets
TLDR
The first datasets of tweets annotated for anger, fear, joy, and sadness intensities are created using a technique called best–worst scaling (BWS) that improves annotation consistency and obtains reliable fine-grained scores.
Combining strengths, emotions and polarities for boosting Twitter sentiment analysis
TLDR
An approach for boosting Twitter sentiment classification using different sentiment dimensions as meta-level features is proposed, which combines aspects such as opinion strength, emotion and polarity indicators, generated by existing sentiment analysis methods and resources.
Determining Word-Emotion Associations from Tweets by Multi-label Classification
TLDR
This paper proposes a methodology for expanding the NRC word-emotion association lexicon for the language used in Twitter using multi-label classification of words and shows that the expanded lexicon achieves major improvements over the original lexicon when classifying tweets into emotional categories.
Proof-of-Learning: A Blockchain Consensus Mechanism Based on Machine Learning Competitions
TLDR
WekaCoin, a peer-to-peer cryptocurrency based on a new distributed consensus protocol called Proof-of-Learning, aims to alleviate the computational waste involved in hashing-based puzzles and to create a public distributed and verifiable database of state- of-the-art machine learning models and experiments.
Positive, Negative, or Neutral: Learning an Expanded Opinion Lexicon from Emoticon-Annotated Tweets
TLDR
The experimental results show that the supervised framework for expanding an opinion lexicon for tweets outperforms the three-dimensional word-level polarity classification performance obtained by semantic orientation, a state-of-the-art measure for establishing world-level sentiment.
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