Datasets:

ccosme
/

FiReCS

Tasks:

Text Classification

Zero-Shot Classification

Modalities:

Formats:

Languages:

Size:

Tags:

sentiment analysis

low-resource languages

Libraries:

License:

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ccosme commited on Jul 12, 2023

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@@ -19,7 +19,7 @@ size_categories:
 ### Dataset Summary
-We introduce FiReCS, the first sentiment-annotated corpus of product and service reviews involving Filipino-English code-switching. The final data set is composed of 10,487 reviews with a fairly balanced number per sentiment class. Inter-annotator agreement is high with a Kripendorffs’s α for ordinal metric of 0.83. Three human annotators were tasked to manually label reviews according to three polarity classes: Positive, Neutral, and Negative.
 ### Supported Tasks and Leaderboards
@@ -34,8 +34,8 @@ Sentiment analysis of bilingual text with code-switching / code-mixing.
 ### Data Fields
-* review: a string containing the body of the review
-* label: an integer containing the label encoding of the gold-truth label provided by the human annotators
 #### Label encoding
 * 2 - Positive

 ### Dataset Summary
+We introduce FiReCS, the first sentiment-annotated corpus of product and service reviews involving Filipino-English code-switching. The data set is composed of 10,487 reviews with a fairly balanced number per sentiment class. Inter-annotator agreement is high with a Kripendorffs’s α for ordinal metric of 0.83. Three human annotators were tasked to manually label reviews according to three polarity classes: Positive, Neutral, and Negative.
 ### Supported Tasks and Leaderboards
 ### Data Fields
+* `review`: a string containing the body of the review
+* `label`: an integer containing the label encoding of the gold-truth label provided by the human annotators
 #### Label encoding
 * 2 - Positive