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Datasets of ASONAM-2015 paper "Tweet sentiment: From classification to quantification"

<p>Datasets used for the following ASONAM 2015 paper:<br> ---------------------------------------------------------------------------------------------------<br> Title: Tweet Sentiment: From Classification to Quantification<br> Authors: Wei Gao and Fabrizio Sebastiani<br> Organization: Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar<br> ---------------------------------------------------------------------------------------------------</p> <p>[Content]</p> <p>* SemEval2013, SemEval2014, SemEval2015 datasets:<br> &nbsp; - semeval.train.feature.txt: Training set for learning sentiment models at development stage<br> &nbsp; - semeval.dev.feature.txt: Held-out set for tuning parameters<br> &nbsp; - semeval.train+dev.feature.txt: Training set for learning the final sentiment model<br> &nbsp; - semeval13.test.feature.txt: SemEval2013 test set<br> &nbsp; - semeval14.test.feature.txt: SemEval2014 test set<br> &nbsp; - semeval15.test.feature.txt: SemEval2015 test set<br> &nbsp;&nbsp;<br> * Other datasets: sanders, sst, omd, hcr, gasp<br> &nbsp; - X.train.feature.txt: Training set for learning sentiment models at development stage<br> &nbsp; - X.dev.feature.txt: Held-out set for tuning parameters<br> &nbsp; - X.train+dev.feature.txt: Traing set for learning the final sentiment model<br> &nbsp; - X.test.feature.txt: Test set<br> where X is one of sanders, sst, omd, hcr and gasp.</p> <p>For more details, please refer to the paper.</p> <p><br> [Citation]<br> You can cite the folowing paper when referring to the dataset:</p> <p>@inproceedings{gao2015tweet,<br> &nbsp; title={Tweet sentiment: From classification to quantification},<br> &nbsp; author={Gao, Wei and Sebastiani, Fabrizio},<br> &nbsp; booktitle={2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},<br> &nbsp; pages={97--104},<br> &nbsp; year={2015},<br> &nbsp; organization={IEEE}<br> }</p>

ShareScore

40/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
4
Access
16
Reuse readiness
8
Engagement
4

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