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README.md
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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.
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The label set descriptions are as follows. The respective
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- [Argument component](/nihiluis/argureviews-component-
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- [Specificity](/nihiluis/argureviews-specificity-
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- [Sentiment](/nihiluis/argureviews-sentiment-
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- [Aspect](/nihiluis/argureviews-aspect-
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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.
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The label set descriptions are as follows. The respective DeBERTa models are linked as well.
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- [Argument component](/nihiluis/argureviews-component-deberta_v1): Distinguishes the argumentative component that is used. Can be one of: claim, premise, background.
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- [Specificity](/nihiluis/argureviews-specificity-deberta_v1): Differentiates between generic statements and more thoughtful user statements. Can be one of: general, specific, experience.
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- [Sentiment](/nihiluis/argureviews-sentiment-deberta_v1): A positive, balanced, negative or neutral argumentative statement about the reviewed item.
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- [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.
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