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2 results for “tweet sentiment quantification”
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> - semeval.train.feature.txt: Training set for learning sentiment models at development stage<br> - semeval.dev.feature.txt: Held-out set for tuning parameters<br> - semeval.train+dev.feature.txt: Training set for learning the final sentiment model<br> - semeval13.test.feature.txt: SemEval2013 test set<br> - semeval14.test.feature.txt: SemEval2014 test set<br> - semeval15.test.feature.txt: SemEval2015 test set<br> <br> * Other datasets: sanders, sst, omd, hcr, gasp<br> - X.train.feature.txt: Training set for learning sentiment models at development stage<br> - X.dev.feature.txt: Held-out set for tuning parameters<br> - X.train+dev.feature.txt: Traing set for learning the final sentiment model<br> - 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> title={Tweet sentiment: From classification to quantification},<br> author={Gao, Wei and Sebastiani, Fabrizio},<br> booktitle={2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},<br> pages={97--104},<br> year={2015},<br> organization={IEEE}<br> }</p>
Datasets of the article "From Classification to Quantification in Tweet Sentiment Analysis"
<p>Datasets used for the following SNAM paper:<br> ---------------------------------------------------------------------------------------------------<br> Title: From Classification to Quantification in Tweet Sentiment Analysis<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> - semeval.train.feature.txt: Training set for learning sentiment models at development stage<br> - semeval.dev.feature.txt: Held-out set for tuning parameters<br> - semeval.train+dev.feature.txt: Training set for learning the final sentiment model<br> - semeval13.test.feature.txt: SemEval2013 test set<br> - semeval14.test.feature.txt: SemEval2014 test set<br> - semeval15.test.feature.txt: SemEval2015 test set<br> <br> * Other datasets: semeval2016, sanders, sst, omd, hcr, gasp, wa, wb<br> - X.train.feature.txt: Training set for learning sentiment models at development stage<br> - X.dev.feature.txt: Held-out set for tuning parameters<br> - X.train+dev.feature.txt: Training set for learning the final sentiment model<br> - X.test.feature.txt (or X.dev-test.feature.txt for semeval2016 only): Test set<br> where X is one of semeval2016, sanders, sst, omd, hcr and gasp.</p> <p>* Training files are saved in ./data/train directory, and held-out and test files are in ./data/test directory</p> <p><br> For more details, please refer to the paper.</p> <p><br> [Citation]<br> You can cite the following paper when referring to the dataset:</p> <pre>@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} }</pre> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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