Datasets:

fhamborg
/

news_sentiment_newsmtsc

@@ -23,14 +23,12 @@ task_ids:
 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 two subsets (`rw` and `mt`), each consisting of three splits (train, validation, and test). We recommend to use the `rw` subset, which is also the default subset. Both subsets share the same train set, in which the three sentiment classes have similar frequency since we applied class boosting. The two subsets differ in their validation and test sets: `rw` contains validation and test sets that resemble real-world distribution of sentiment in news articles. In contrast, `mt`'s validation and test sets contain only sentences that each have two or more (different) targets, where each target's sentiment was labeled individually.
 More information on the subsets 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**.
 1. **`sentence_normalized`: a single sentence**
@@ -72,12 +70,10 @@ An example looks like this:
 ## Contact
-If you want to get in touch, feel free to contact Felix Hamborg. If you find an issue with the dataset or model or have a question concerning either, please open an issue in the repository.
-* Web: [https://felix.hamborg.eu/](https://felix.hamborg.eu/)
-* Mail: [felix.hamborg@uni-konstanz.de](mailto:felix.hamborg@uni-konstanz.de)
 * Repository: [https://github.com/fhamborg/NewsMTSC](https://github.com/fhamborg/NewsMTSC)
 ## How to cite

 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).
+## Subsets and splits
 The dataset consists of two subsets (`rw` and `mt`), each consisting of three splits (train, validation, and test). We recommend to use the `rw` subset, which is also the default subset. Both subsets share the same train set, in which the three sentiment classes have similar frequency since we applied class boosting. The two subsets differ in their validation and test sets: `rw` contains validation and test sets that resemble real-world distribution of sentiment in news articles. In contrast, `mt`'s validation and test sets contain only sentences that each have two or more (different) targets, where each target's sentiment was labeled individually.
 More information on the subsets 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**.
 1. **`sentence_normalized`: a single sentence**
 ## Contact
+If you find an issue with the dataset or model or have a question concerning either, please open an issue in the repository.
 * Repository: [https://github.com/fhamborg/NewsMTSC](https://github.com/fhamborg/NewsMTSC)
+* Web: [https://felix.hamborg.eu/](https://felix.hamborg.eu/)
 ## How to cite