{'fraction_non_alphanumeric': 0.013761467889908258, 'fraction_numerical': 0.0030581039755351682, 'mean_word_length': 5.853403141361256, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Propaganda Detection Using Sentiment Aware Ensemble Deep Learning

In today's highly globalized world with vast information transfers, it is increasingly difficult to distinguish valid information from attempts to manipulate human attitude through propaganda, which poses a growing threat due to its spread and sophistication. This paper proposes a deep learning method in order to combine sentiment scores with traditional Word2Vec vectors which result in a sentiment aware representation containing semantic and emotional information, which, when used together, result in a more accurate propaganda classification model. The Word2Vec vector method is a useful tool used to recognize the semantic meaning of words and their structures in natural language processing, i.e., their emotional classification, and thus to detect propaganda. An emotional dictionary built into VADER's sentiment analysis results in a text sentiment score representing emotional information. This method preserves the flexibility of the Word2Vec vector by combining it with an output of sentiment analysis. Tests conducted using a Word2Vec model without sentiment data and using sentiment data with standard deep learning methods for propaganda detection show that this hybrid approach increases propaganda classification accuracy.