Context-Based Persuasion Analysis of Sentiment Polarity Disambiguation in Social Media Text Streams

Abstract

Bayesian belief network is an effective and practical approach that is widely acceptable for real-time series prediction and decision making. However, its computational efforts and complexity increased exponentially with increased number of states. Hence, this research paper a proposed approach inspired by context-based persuasion analysis of sentiment analysis and its impact on the propagation of false information is designed. As social media text consist of unwanted information and needs to be addressed including effective polarity prediction of a sentimentwise ambiguous word in generic contexts. Therefore, in proposed approach persuasion-based strategy based on social media crowd is considered for analyzing the impact of sentimental contextual polarity in social media including pre-processing. For analyzing the polarity of sentiment, Bayesian belief network is used, whereas Turbo Parser is implemented for visual representation of diverse feature class and spontaneous hold of the relationships between features. Furthermore, to analyze the lexicons dependency on each word in terms of context, a tree-based dependency parser representation is used to count the dependency score. Features associated with sentimental words are extracted using Penn tree bank for sentiment polarity disambiguation. Therefore, a graphical model known as Bayesian network learning is opted to design a proposed approach which take care the dependency among various lexicons. Various predictors, namely, (1) pre-processing and subjectivity normalization, (2) computation of threshold and persuasion factor, and (3) extraction of sentiments from dependency parsing from the retrieved text are introduced. The findings of this study indicate that it is most important to compute the local and global context of various sentimental words to analyze the polarity of text. Furthermore, we have tested our proposed method with a standard data set and a real case study is also implemented based on COVID-19, Olympics-2020 and Russia–Ukraine war for the feasibility analysis of the proposed approach. The findings of this study imply a complex and context-dependent mechanism behind the sentiment analysis which shed lights on the efforts for resolving contextual polarity disambiguation in social media.