Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Source Datasets:
original
License:
Update README.md
Browse files
README.md
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@@ -37,36 +37,12 @@ Each split is stored in a JSONL file. In JSONL, each line represents one JSON ob
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Each target in `targets` consists of:
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1. `
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2. **`
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3. **`
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4.
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5. **`
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6. `
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An example looks like this:
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```
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{
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"primary_gid":"allsides_1192_476_17_— Judge Neil M. Gorsuch_126_139",
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"sentence_normalized":"But neither side harbored any doubts, based on the judge’s opinions, other writings and the president who nominated him, that Judge Gorsuch would be a reliable conservative committed to following the original understanding of those who drafted and ratified the Constitution.",
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"targets":[
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{
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"Input.gid":"allsides_1192_476_17_— Judge Neil M. Gorsuch_126_139",
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"from":126,
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"to":139,
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"mention":"Judge Gorsuch",
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"polarity":6.0,
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"further_mentions":[
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{
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"from":116,
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"to":119,
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"mention":"him"
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}
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]
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}
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]
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}
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```
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## Contact
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Each target in `targets` consists of:
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1. `mention`: text of the mention within `sentence`
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2. **`polarity`: sentiment of the sentence concerning the target's mention (-1 = negative, 0 = neutral, 1 = positive)**
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3. **`from`: character-based, 0-indexed position of the first character of the target's mention within `sentence`**
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4. **`to`: last character of the target's mention**
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5. **`sentence`: sentence**
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6. `id`: identifier that is unique within NewsMTSC
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## Contact
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