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Increasing popularity of social networks like LinkedIn, MySpace and other networks in present days. Communication is also increased in between users present in social networks. Large amount of data being move on social media because of increase data outsourcing. Sentiment analysis is impressive and interest concept for online social networks, while different types of existing methods to find sentiment in online social networks to define communication between different users to categorize patterns with respect to similar attributes to analyze large data. We present and suggest the Hybrid Machine Learning method in this paper.(which is combination of Balanced Window and Classification based on Parts of Speech) to handle outsourced data of social networks from Face Book and other blogging services are trained and then classify the relation based on emotional aspect like positive or negative and other relations in social streams. The performance of our proposed approach is to extensively close to machine learning and identify important relevant features randomly and perform sentiment analysis in different data streams. Our experimental results show exhaustive level of classification results with comparison of existing approaches in real time environment.

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.