Leveraging Cloud Computing and Microservices for Tweet Analysis

Abstract

Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking site like Twitter is a widely used platform where millions of tweets are tweeted every day and most of these tweets never reach their intended audiences and fail fulfill their purposes because they are lost in a huge sea of tweets that are often irrelevant. Analysis of a segment of tweets might not truly reflect the real sentiments of the overall tweets on a topic which is a challenge. To tackle this problem, we introduce efficient techniques with which tweets are extracted, translated and sentiment analysis is performed on both text and images. These results are shown graphically and tabularly with other useful and important data such as username and hashtags used in tweets. For controlled access and security, login and registration features are incorporated.