Twitter Based Capital Market Analysis Using Cloud Statistics

Abstract

People in the modern world are attracted towards smart working and earning environments rather than having a long-term perception. The goal of this work is to address the challenge of providing better inputs to the customers interested to investing in the share market to earn better returns on investments. The Twitter social networking site is chosen to develop the proposed environment as a majority of the customers tweet about their opinions. A huge set of data across various companies that take inputs from Twitter are processed and stored in the cloud environment for efficient analysis and assessment. A statistical measure is used to signal the worth of investing in a particular stock based on the outcomes obtained. Also, rather than ignoring the missing values and unstructured data, the proposed work analyzes every single entity to enable the customers to take worthy decisions. Tweets in the range of 1 to 100,000 are taken to perform analysis and it is observed from the results that for a maximum of 100,000 tweets, the number of missing is identified as 2,524 and the statistical measure to fill in the missing values is calculated based on the particular missing data record, the count of all data records, and the total number of records. If the outcome of the measure is obtained as a negative, then proceeding with an investment is not recommended. The findings of this work will help the share market investors to earn better profits.