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SemEval-2017 Task 4: Sentiment Analysis in Twitter

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arxiv 1912.00741 v1 pith:MUYW3CUI submitted 2019-12-02 cs.CL cs.IRcs.LG

classification cs.CLcs.IRcs.LG
keywords sentimenttasktwitteranalysiscontinuesfive-pointmadeordinal
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii)~we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.

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  1. Quantum Graph Transformer for NLP Sentiment Classification

    cs.CL 2025-06 conditional novelty 5.0 of 10

    A hybrid quantum-classical graph transformer for sentiment classification reports higher accuracy and better sample efficiency than a classical graph transformer on five small benchmark datasets.

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