ANALYSIS OF DIGITAL MARKETING DATASET USING MACHINE LEARNING ALGORITHM

Abstract

Recent years have seen opinion analyzes on Twitter becoming a common trend for science. Most existing Twitter sentiment analysis solutions essential to understand only organized Twitter message information. The performance of tweets or not good at all time because some tweets or not having one obvious meaning. Research indicate that the emotion transmission patterns in Twitter have connections to the polarities of emotional Twitter posts.This paper focus on how to analysis of digital marketing data set using machine learning algorithm. The diffusion of sentiments by studying a fact or situation that is observed called reversal sentiment. Then the look at the interrelationships between twitter’s textual awareness and patterns of feeling diffusion and suggest an iterative algorithm to conclude the polarities in twitter posts. This study is to help and improve an interpretation of twitter’s feelings.We suggested learning algorithms for machines running SVM and Random Forest. We compare traditional algorithms such as the Nave bayes machine learning algorithm to make successful layout. Measurement of evaluation was taken using accuracy, precision and F1.The Social Framework was developed for business purposes and checked with end-users for effective implementation. Machine learning algorithm has found the best algorithm, and work is done to block inappropriate comments in twitter