Home

Awesome

Sentiment Political Compass

One answer to biased news and false information is transparancy and quantifiablity. For this reason, we introduce the Sentiment Political Compass, a data-driven framework to analyze newspapers with respect to their political conviction.

@article{falcksentiment, 
	title={Sentiment Political Compass: A Data-driven Analysis
	       of Online Newspapers regarding Political Orientation}, 
	author={Falck, Fabian and Marstaller, Julian and Stoehr, Niklas and 
	       Maucher, S{\"o}ren and Ren, Jeana and Thalhammer, Andreas 
	       and Rettinger, Achim and Studer, Rudi}, 
	booktitle={The Internet, Policy & Politics Conference 2018}, 
	year={2018}, 
}

Overview

Resources

Data Exploration

In order to explore the data and the code for creating the plots, we provide you with a jupyter notebook. If you just want to see charts, please refer to folder Charts.

Pipeline Exploration

If you want to replicate a Sentiment Polical Compass for different newspapers, times or countries, please clone this repository and refer to the section Code. All code was written in Python Version 3.6.4

git clone https://github.com/JulianMar11/SentimentPoliticalCompass
cd SentimentPoliticalCompass/
pip install -r requirements.txt

Further instructions of the pipeline steps are provided in the subfolders for the processing steps.

Dataset structure

URL information

ColumnTypeExplanation
startdateDatebeginning of time range used in url-crawling
enddateDateending of time range used in url-crawling
dateDateassumed date of article, also used as index
datetimedeltaDatesearch range
newspaperCategoryname of newspaper
search_termCategoryname of searched entity
response_idIdentifieridentifier of search result
urlURLarticle url

Sentiment information (IBM NLU API)

ColumnTypeExplanation
entity_textStringArticle text of entity
entity_typeCategoryType of entity, (doc)
countIntMentions in article
relevanceFloatRelevance of entity in text
sentimentFloatSentiment of entity in range -1, 1
sentimentcategoryCategorynegative, positive or neutral sentiment
sentiment_idIdentifieridentifier of sentiment

Match information

ColumnTypeExplanation
levenshteinIntLevenshtein distance of matched politician
match_typeCategory1 = full match and searched politician <br /> 2 = full match but not searched politician <br /> 3 = surname match and searched politician <br /> 4 = surname match but not searched politician
politicianCategoryName of matched politician
partyCategoryParty of matched politician
roleCategoryPolitical role of matched politician

Contributors

Fabian Falck, Julian Marstaller, Niklas Stoehr, Soeren Maucher, Jeana Ren, Andreas Thalhammer, Achim Rettinger, Rudi Studer

License

The MIT License (MIT)

Copyright (c) 2018 Fabian Falck, Julian Marstaller, Niklas Stoehr, Soeren Maucher, Jeana Ren, Andreas Thalhammer, Achim Rettinger, Rudi Studer

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.