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config.ini
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config.ini
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[ArticleSelection]
# Selection of relevant articles based on the search_words
run_article_selection = True
input_path_base = data/ta_scrape100k_
output_base = data/relevant_articles_
search_words = flüchtling, migra, einwander, geflüchtete, asyl
start_year = 2007
end_year = 2019
# append to existing file or write new file:
append_to_existing_file = False
# sample part of the files for annotation
# if true write three annotation files
use_annotation = False
training_size = 1200
seed = 0
[Analysis]
# Input data
# normaly output of ArticleSelection
input_file = data/relevant_articles_evaluation.json
# Word to Vec Evaluation
run_w2v = True
run_by_year = True
run_by_publisher = False
run_by_publisher_by_year = False
number_most_sim = 50
start_year = 2007
end_year = 2019
search_words_w2v = flüchtling
output_base_w2v = data/most_similar
# Sentiment Analysis
run_senti = True
senti_methods = sentiws, generic_sentibert, finetuned_sentibert
finetuned_sentibert_path = mdraw/german-news-sentiment-bert
output_senti = data/sentiment_analysis_results_full.json
search_words = flüchtling, migra, einwander, geflüchtete, asyl
# Quantitative evaluation of different methods
run_senti_eval = True
senti_eval_input = data/validation.csv
senti_eval_output = data/sentiment_analysis_eval_metrics.json
[Plotting]
sentiment_plot = True
input_file = data/sentiment_analysis_results_full.json
[WordClouds]
wordcloud_plot = True
input_file = data/most_similar_by_year.json
output_path = data/
words = flüchtling
#column: publishers or years
column_values = 2013, 2014, 2015
#column_values = welt, bild
number_of_words_in_wordcloud = 10