Sentiment Corpus for Swedish 🇸🇪 Norwegian 🇳🇴 Danish 🇩🇰 Finnish 🇫🇮 (and English 🏴)
The corpus is crawled from se.trustpilot.com, no.trustpilot.com, dk.trustpilot.com, fi.trustpilot.com and trustpilot.com. It consists of reviews from all the 22 corresponding categories:
categories = ['animals_pets', 'electronics_technology', 'events_entertainment', 'vehicles_transportation',
'business_services', 'health_medical', 'home_garden', 'hobbies_crafts', 'home_services',
'legal_services_government', 'construction_manufactoring', 'food_beverages_tobacco', 'media_publishing',
'money_insurance', 'travel_vacation', 'restaurants_bars', 'public_local_services', 'shopping_fashion',
'education_training', 'beauty_wellbeing', 'sports', 'housing_utility_company']The size for each language is 10 000 texts evenly balanced between positive and negative reviews. A positive review is considered as a text with the rating 4 or 5, and a negative review is rated as 1 or 2. The texts rated as 3 were not used. The zip files consist of csv files for each language with the columns text and label, were label == 1 is a positive review and label == 0is a negative review.
For our paper: Should we Stop Training More Monolingual Models, and Simply Use Machine Translation Instead? we used the first 7500 texts for training and the last 2500 texts for evaluating.
ScandiSent.zip 🇸🇪 🇳🇴 🇩🇰 🇫🇮 + 🏴
Is the raw data for each language where we used fastText language identification to ensure that the texts were of the right language.
ScandiSent-mt.zip 🏴
Consists of the raw data from ScandiSent machine translated to English 🏴 using Googles Neural Machine Translation API.
2021-02-06