A comparative media-bias pipeline on 37 news outlets using hierarchical topics, sentiment, entities, and LLM ontologies was tested, with clear signals from titles and entities but failures in body sentiment and larger ontology comparisons.
Detecting Potential Topics In News Using BERT, CRF and Wikipedia
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abstract
For a news content distribution platform like Dailyhunt, Named Entity Recognition is a pivotal task for building better user recommendation and notification algorithms. Apart from identifying names, locations, organisations from the news for 13+ Indian languages and use them in algorithms, we also need to identify n-grams which do not necessarily fit in the definition of Named-Entity, yet they are important. For example, "me too movement", "beef ban", "alwar mob lynching". In this exercise, given an English language text, we are trying to detect case-less n-grams which convey important information and can be used as topics and/or hashtags for a news. Model is built using Wikipedia titles data, private English news corpus and BERT-Multilingual pre-trained model, Bi-GRU and CRF architecture. It shows promising results when compared with industry best Flair, Spacy and Stanford-caseless-NER in terms of F1 and especially Recall.
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cs.AI 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Unraveling Media Perspectives: A Comprehensive Methodology Combining Large Language Models, Topic Modeling, Sentiment Analysis, and Ontology Learning to Analyse Media Bias
A comparative media-bias pipeline on 37 news outlets using hierarchical topics, sentiment, entities, and LLM ontologies was tested, with clear signals from titles and entities but failures in body sentiment and larger ontology comparisons.