An analytic framework for enhancing the performance of big heterogeneous data analysis

The use of social media networks is becoming a current phenomenon in the world today where people are sharing posts and tweets, connect with different groups, and share their opinions about things. This data is extremely heterogeneous and so it is hard to analyze and derive information from this data that is considered an indispensable source for decision-makers. New techniques are therefore needed to handle these huge amounts of data to find the hidden information thus improve the results of the analysis. We are developing a framework for the analysis of heterogeneous data using machine learning (ML) techniques. In contrast to most of the literature frameworks that focus on a specific type of heterogeneous data for evaluating the proposed framework, we have analyzed 15k tweets data from six American airlines. These tweets are collected from the open stream of Twitter, also predict, classify each tweet as a negative or positive review, and test the ability of deep learning (DL) algorithms by comparing it with traditional ML algorithms. The findings confirmed the validity of the proposed framework and helped to achieve the study objective by providing excellent analysis performance and provide insights into additional aspects of information extraction from heterogeneous data.