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Datasets:
Alienmaster
/
omp_sa

Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Languages:
German
Size:
1K<n<10K
Tags:
Sentiment Analysis
Libraries:
Datasets
pandas
Croissant
License:
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omp_sa / README.md
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metadata
annotations_creators:
  - expert-generated
language_creators:
  - crowdsourced
language:
  - de
license:
  - cc-by-nc-sa-4.0
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
tags:
  - Sentiment Analysis
task_categories:
  - text-classification
pretty_name: One Million Posts Corpus - Sentiment Subset
configs:
  - config_name: default
    column_names:
      - ID_Post
      - Headline
      - Body
      - Category
    data_files:
      - split: full
        path: full.csv

Dataset Card for One Million Posts Corpus - Sentiment Subset

Dataset Description

Dataset Summary

The “One Million Posts” corpus is an annotated data set consisting of user comments posted to an Austrian newspaper website (in German language).

This subset of the original dataset only containing Post IDs, Headlines and Bodys of Posts with the Sentiment label.

The Sentiment labels are renamed to "Positive", "Negative" and "Neutral" for convenience.

If you are intrested in the full dataset use the official dataset on huggingface.

Licensing Information

This data set is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Citation Information 

@InProceedings{Schabus2018,
  author    = {Dietmar Schabus and Marcin Skowron},
  title     = {Academic-Industrial Perspective on the Development and Deployment of a Moderation System for a Newspaper Website},
  booktitle = {Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC)},
  year      = {2018},
  address   = {Miyazaki, Japan},
  month     = may,
  pages     = {1602-1605},
  abstract  = {This paper describes an approach and our experiences from the development, deployment and usability testing of a Natural Language Processing (NLP) and Information Retrieval system that supports the moderation of user comments on a large newspaper website. We highlight some of the differences between industry-oriented and academic research settings and their influence on the decisions made in the data collection and annotation processes, selection of document representation and machine learning methods. We report on classification results, where the problems to solve and the data to work with come from a commercial enterprise. In this context typical for NLP research, we discuss relevant industrial aspects. We believe that the challenges faced as well as the solutions proposed for addressing them can provide insights to others working in a similar setting.},
  url       = {http://www.lrec-conf.org/proceedings/lrec2018/summaries/8885.html},
}