Sentiment Analysis for Smartphone Operating System: Privacy and Security on Twitter Data

The aim of the study was to investigate the privacy and security of the user data on Twitter. For gathering the essential information, more than two million relevant tweets through the span of two years were used to conduct the study. In addition, we are classifying sentiment of Twitter data by exhibiting results of a machine learning by using the Naive Bayes algorithm. Although this algorithm is time consuming compared to the listing method yet can lead to effective estimation relatively. The tweets are extracted and pre-processed and then categorized them in neutral, negative and positive sentiments. By applying the chosen methodology, the study would end up in identifying the most effective mobile operating systems according to the sentiments of social media users. Additionally, the application of the algorithm needs to meet the privacy and security needs of Twitter users in order to optimize the use of social media intelligence. The approach will help in assessing the competitive intelligence of the Twitter data and the challenges in the form of privacy and- security of the user content and their contextual information simultaneously. The findings of the empirical research show that users are more concerned about the privacy and security of iOS compared to Android and Windows phone.