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Dataset . 2015
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ZENODO
Dataset . 2015
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2015
License: CC BY
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Datasets of the article "From Classification to Quantification in Tweet Sentiment Analysis"

Authors: Gao, Wei; Sebastiani, Fabrizio;

Datasets of the article "From Classification to Quantification in Tweet Sentiment Analysis"

Abstract

Datasets used for the following SNAM paper: --------------------------------------------------------------------------------------------------- Title: From Classification to Quantification in Tweet Sentiment Analysis Authors: Wei Gao and Fabrizio Sebastiani Organization: Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar --------------------------------------------------------------------------------------------------- [Content] * SemEval2013, SemEval2014, SemEval2015 datasets: - semeval.train.feature.txt: Training set for learning sentiment models at development stage - semeval.dev.feature.txt: Held-out set for tuning parameters - semeval.train+dev.feature.txt: Training set for learning the final sentiment model - semeval13.test.feature.txt: SemEval2013 test set - semeval14.test.feature.txt: SemEval2014 test set - semeval15.test.feature.txt: SemEval2015 test set * Other datasets: semeval2016, sanders, sst, omd, hcr, gasp, wa, wb - X.train.feature.txt: Training set for learning sentiment models at development stage - X.dev.feature.txt: Held-out set for tuning parameters - X.train+dev.feature.txt: Training set for learning the final sentiment model - X.test.feature.txt (or X.dev-test.feature.txt for semeval2016 only): Test set where X is one of semeval2016, sanders, sst, omd, hcr and gasp. * Training files are saved in ./data/train directory, and held-out and test files are in ./data/test directory For more details, please refer to the paper. [Citation] You can cite the following paper when referring to the dataset: @article{gao2016classification, title={From classification to quantification in tweet sentiment analysis}, author={Gao, Wei and Sebastiani, Fabrizio}, journal={Social Network Analysis and Mining}, volume={6}, number={1}, pages={19}, year={2016}, publisher={Springer} }

Keywords

text quantification, tweet sentiment quantification

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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