| language: | |
| - en | |
| dataset_info: | |
| features: | |
| - name: type | |
| dtype: string | |
| - name: text | |
| dtype: string | |
| - name: annotator | |
| dtype: string | |
| - name: component | |
| dtype: string | |
| - name: specificity | |
| dtype: string | |
| - name: sentiment | |
| dtype: string | |
| - name: aspect | |
| dtype: string | |
| - name: id | |
| dtype: string | |
| - name: sidx | |
| dtype: float64 | |
| splits: | |
| - name: train | |
| num_bytes: 1328357 | |
| num_examples: 7266 | |
| download_size: 534609 | |
| dataset_size: 1328357 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| # Dataset Card for "argureviews" | |
| Dataset for basic argumentation in online reviews | |
| The dataset aims to annotate online review sentences for basic argumentative quality, sentiment and aspect of interest. It covers 1016 online reviews with 7286 sentences for the following domains: products from Amazon, local services, restaurant and hotels from Yelp and brokerage apps from the Google Play Store. | |
| The label set descriptions are as follows. The respective DeBERTa models are linked as well. | |
| - [Argument component](/nihiluis/argureviews-component-deberta_v1): Distinguishes the argumentative component that is used. Can be one of: claim, premise, background. | |
| - [Specificity](/nihiluis/argureviews-specificity-deberta_v1): Differentiates between generic statements and more thoughtful user statements. Can be one of: general, specific, experience. | |
| - [Sentiment](/nihiluis/argureviews-sentiment-deberta_v1): A positive, balanced, negative or neutral argumentative statement about the reviewed item. | |
| - [Aspect](/nihiluis/argureviews-aspect-deberta_v1): Provides more insight into what aspect of interest the argumentative statement covers. Can be one or more of: general sentiment, price, delivery, function and quality, fun and usage, style, installation, customer service and none. Only available for the Amazon review subset. |