An Analysis of Employees’ Email Data That Can Cause Conspiracy

Abstract

The sentiment analysis is a cutting-edge technique for accessing internet data and these data has been a growing discipline of the data mining and machine learning researchers and academics for the last decades. Hence, sentiment analysis on employees Email data has not been studied comprehensively. The main objective of the study to presents a method to email sentiment analysis using an application that can spontaneously find out the conspiracy among the employees by analysis their email records. In our study we used a popular TFIDF approach to classify the conversion over email data. We evaluated the performance of a prominent machine learning algorithm which is “Logistic Regression (LR)”. The performance of the supervised-based techniques was examined with confusion matrix. In this experiment, our model achieved the accuracy of 82.45% overall to classify the employee’s conversion in real time. Our findings show that the Logistics Regression techniques outperformed to the detect of email conversation of the employees. Therefore, our study has highlighted the research studies and possibilities in the field of text data and sentiment study by machine learning techniques.