Rumor Detection in Business Reviews Using Supervised Machine Learning
Source of Publication
Proceedings - 2018 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing, BESC 2018
© 2018 IEEE. Currently, a high volume of business data is generating with a high velocity in different forms like unstructured, structured or semi-structured. Due to social media arrival, there is a deluge of business rumors and their manual screening is time-consuming and difficult. In the current social computing era, it is necessary to move towards an automated process for the detection of business rumors. This work aims at developing an automated system for detecting business rumors from online business reviews using supervised machine learning classifiers, namely Logistic Regression, Support Vector Classifier (SVC), Naïve Bayesian (NB), K-Nearest Neighbors (KNN) to classify the business reviews into rumor and nonrumor. Experimental results show that Naïve Bayesian (NB), achieved efficient results with respect to other classifiers with an accuracy of 72.43 %.
Institute of Electrical and Electronics Engineers Inc.
Business intelligence, Rumors, Supervised machine learning
Habib, Ammara; Akbar, Saima; Asghar, Muhammad Zubair; Khattak, Asad Masood; Ali, Rahman; and Batool, Ulfat, "Rumor Detection in Business Reviews Using Supervised Machine Learning" (2018). All Works. 3009.
Indexed in Scopus