Summary of the paper

Title NoReC: The Norwegian Review Corpus
Authors Erik Velldal, Lilja Øvrelid, Eivind Alexander Bergem, Cathrine Stadsnes, Samia Touileb and Fredrik Jørgensen
Abstract This paper presents the Norwegian Review Corpus (NoReC), created for training and evaluating models for document-level sentiment analysis. The full-text reviews have been collected from major Norwegian news sources and cover a range of different domains, including literature, movies, video games, restaurants, music and theater, in addition to product reviews across a range of categories. Each review is labeled with a manually assigned score of 1--6, as provided by the rating of the original author. This first release of the corpus comprises more than 35,000 reviews. It is distributed using the CoNLL-U format, pre-processed using UDPipe, along with a rich set of metadata. The work reported in this paper forms part of the SANT initiative (Sentiment Analysis for Norwegian Text), a project seeking to provide open resources and tools for sentiment analysis and opinion mining for Norwegian.
Topics Opinion Mining / Sentiment Analysis, Document Classification, Text Categorisation, Corpus (Creation, Annotation, Etc.)
Full paper NoReC: The Norwegian Review Corpus
Bibtex @InProceedings{VELLDAL18.851,
  author = {Erik Velldal and Lilja Øvrelid and Eivind Alexander Bergem and Cathrine Stadsnes and Samia Touileb and Fredrik Jørgensen},
  title = "{NoReC: The Norwegian Review Corpus}",
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {May 7-12, 2018},
  address = {Miyazaki, Japan},
  editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {979-10-95546-00-9},
  language = {english}
Powered by ELDA © 2018 ELDA/ELRA