SENTINET: a deep sentiment analysis network for political media bias detection

Abstract

but unfortunately it also has high media bias rates and one of the lowest press freedom rankings for democracies. Media bias plays an influencing role even at the voting booth as propaganda can skew voter decisions and perceptions of what is true in this era of fake news. It’s vital to keep an eye on bias in the news and to provide a platform where people can get unbiased and reliable news. Researchers in sentiment analysis and bias detection have been using various techniques to achieve higher accuracy. This study aims Indian political media bias detection by proposing SentiNet - a graphical processing unit (GPU) trained modified convolution neural network (CNN) model consisting of linearly inverted depth-wise separable convolutions capable of classifying news as either ‘unbiased’ or ‘biased’ from Twitter data. Because of its simple architecture and lesser number of tuning parameters, it is observed that SentiNet is a good fit in terms of accuracy and loss function and its training time reduces by 50% when using a GPU. Considering top 5 media news houses, from results it is observed that Channel 1 and Channel 2 emerged as the most biased towards ruling party and Opposition parties respectively. Channel 3 and Channel 5 emerged unbiased towards ruling party with balanced reporting. Channel 4 has emerged as unbiased towards Opposition parties. From Twitter political discourse, it is found that parties discuss themselves or their opposing parties and seldom issues of national interest. Apart from governments, the proposed model can be extended to other social media networks and used by companies to measure customer bias in any product or service.