Datasets:

fhamborg
/

news_sentiment_newsmtsc

Tasks:

Text Classification

Sub-tasks:

sentiment-classification

Languages:

Multilinguality:

Size Categories:

Language Creators:

expert-generated

Annotations Creators:

expert-generated

Source Datasets:

License:

Dataset card Viewer Files Files and versions Community

fhamborg commited on Dec 30, 2021

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@@ -21,18 +21,18 @@ task_ids:
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 # NewsMTSC dataset
-The files contained in this dataset are part of "NewsMTSC" as described in our paper "NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles" published at the EACL 2021.
 ## Dataset
 ### Files
-The dataset consists of three splits. We suggest to use the splits as follows (more information can be found in our [paper](https://aclanthology.org/2021.eacl-main.142.pdf)) as described
 * `train.jsonl` - For **training**.
 * `devtest_rw.jsonl` - To evaluate a model's classification performance on a "**real-world**" set of sentences, i.e., the set was created with the objective to resemble real-world distribution as to sentiment and other factors mentioned in the paper.
 * `devtest_mt.jsonl` - To evaluate a model's classification performance only on sentences that contain at least **two target mentions**. Note that the mentions were extracted to refer to different persons but in a few cases might indeed refer to the same person.
-**For most projects, we suggest** to use `train.jsonl` for training and `devtest_rw.jsonl` for evaluation.
 ### Format
 Each split is stored in a JSONL file. In JSONL, each line represents one JSON object. In our dataset, each JSON object consists of the following attributes. When using the dataset, you most likely will need (only) the attributes highlighted in **bold**.

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 # NewsMTSC dataset
+NewsMTSC is a high-quality dataset consisting of more than 11k manually labeled sentences sampled from English news articles. Each sentence was labeled by at least five human coders. The dataset is published as our [EACL 2021 paper *NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles*](https://aclanthology.org/2021.eacl-main.142.pdf).
 ## Dataset
 ### Files
+The dataset consists of three splits. We suggest to use the splits as follows:
 * `train.jsonl` - For **training**.
 * `devtest_rw.jsonl` - To evaluate a model's classification performance on a "**real-world**" set of sentences, i.e., the set was created with the objective to resemble real-world distribution as to sentiment and other factors mentioned in the paper.
 * `devtest_mt.jsonl` - To evaluate a model's classification performance only on sentences that contain at least **two target mentions**. Note that the mentions were extracted to refer to different persons but in a few cases might indeed refer to the same person.
+**For most projects, we suggest** to use `train.jsonl` for training and `devtest_rw.jsonl` for evaluation. More information can be found in our [paper](https://aclanthology.org/2021.eacl-main.142.pdf).
 ### Format
 Each split is stored in a JSONL file. In JSONL, each line represents one JSON object. In our dataset, each JSON object consists of the following attributes. When using the dataset, you most likely will need (only) the attributes highlighted in **bold**.