A Proposed Framework for Improving Analysis of Big Unstructured Data in Social Media

With the rapid development of Big Data and the necessity for analyzing their huge volumes, the issue of Unstructured Data analysis in social media was appeared. The Data analysis process is very important in all fields as to make decisions at the right time and over certain facts. The usage of social media has become the latest trend in today's world in which users send, read posts known as `message' and communicate with various groups. Users are sharing their regular life, posting their views on everything like products and locations. This data is extremely unstructured, making it hard to analyze. Machine learning technology offers important data preparation techniques for processing large-scale data to extract knowledge, e.g., classifying data. Extract useful information from social media data is essential to success in the big data age. Therefore, fresh strategies are needed for handling huge quantities of unstructured data and finding the hidden information in these data and achieving better data analysis outcomes, In this paper, the proposed framework recommends the construction of a machine-learning model capable of analyzing unstructured text data with highly accuracy compared to other machine learning algorithms.