Political Bias Detection from Hindi News Using Neutrosophic Sets, MuRil and Extended Hindi SentiWordNet

The Media is stated as the “fourth pillar of democracy" that influences day to day life of each individual. The falling rank of India in the World Press Freedom Index (WPFI) portrays biasness in news articles, especially for political news articles. The biased headlines morph the perception of the people regarding the topic of interest and fill them up with baseless prejudice. Currently, thousands of Hindi newspaper publishers in India have more than 100 million subscribers. Political bias detection from news is a fresh research area, researchers did not considers contextual analysis yet. This is the first political news bias detection model that considers neutrosophic sets for contextual word analysis to address inconsistency and indeterminacy, newly enhanced Hindi SentiWordNet and MuRil language model for encoded output and multiclass classification. This proposed novel model transforms input words into a neutrosophic domain and is characterized by degree of truth, indeterminacy and false membership components. This set applies the concept of indeterminacy that improves the biasness detection accuracy for each news headline. The experimentation and evaluation of proposed model shows the better accuracy than the state of the art methods, MuRil language model combined with neutrosophic sets improves 37.5% to 50% accuracy.