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ctoraman
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BilTweetNews-sentiment-analysis

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Text Classification

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sentiment analysis

text classification

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ctoraman commited on Nov 29, 2023

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@@ -49,8 +49,8 @@ There are 4 sentiment classes:
 All tweets are labeled by 17 annotators. We provide the normalized distribution of annotations across 4 sentiment classes. We also provide the majority sentiment class at the last column. If there are multiple classes with highest scores, then we set "Multi" as majority.
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 # If you would like to use any material in this repository, please cite the following papers:
 - Toraman, C. Early Prediction of Public Reactions to News Events Using Microblogs. Seventh BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2017), Barcelona, Spain, 5 September 2017.

 All tweets are labeled by 17 annotators. We provide the normalized distribution of annotations across 4 sentiment classes. We also provide the majority sentiment class at the last column. If there are multiple classes with highest scores, then we set "Multi" as majority.
+Github Repo: https://github.com/BilkentInformationRetrievalGroup/BilTweetNews2017
 # If you would like to use any material in this repository, please cite the following papers:
 - Toraman, C. Early Prediction of Public Reactions to News Events Using Microblogs. Seventh BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2017), Barcelona, Spain, 5 September 2017.