Sentiment score-based classification for fake news using machine learning and LSTM-BiLSTM

Abstract

Fake news creates social turbulence, which may hamper our social or economic equilibrium. Researchers have harnessed machine learning (ML) and deep learning (DL) algorithms to combat this challenge, particularly in disparate environments. Numerous techniques have been created to classify false news based on various textual features, including deep learning, machine learning, and evolutionary methodologies. Although fake news sentiment analysis is not entirely new, sentiment score-based artificial news analysis is rarely used. Our method incorporates machine learning techniques and deep learning techniques, such as LSTM-BiLSTM, with SentiWordNet parser-obtained sentiment scores. This integration improves feature sets and enables a more detailed analysis of emotional context. This research pioneers using machine learning along with deep learning techniques based on sentiment scores, an innovative approach within the field. Our research substantially improves the detection of false news. Recall and F-measure are significantly enhanced using machine learning techniques with the COVID-19 dataset. Moreover, sentiment-based deep learning techniques used for both the LIAR and COVID-19 datasets surpass previous benchmarks, obtaining a remarkable accuracy improvement of over 15% on the LIAR dataset compared to existing literature. This pioneering sentiment score-based approach enhances fake news detection accuracy, offering a potent tool to counter misinformation and safeguard societal equilibrium.