Significance of machine learning algorithms in professional blogger's classification. (January 2018) - Asim, Yousra; Shahid, Ahmad Raza; Malik, Ahmad Kamran; Raza, Basit
Highlights: Blogger's data is categorized into professional and seasonal bloggers using machine learning algorithms. Decision tree algorithms, lazy learning methods and ensembling techniques are applied to standard datasets to analyze and compare the data classification algorithms on nominal data. Random Forest and Nearest-neighbor classifiers achieved high accuracy for data classification. Importance of decision tree rules for factor identification behind the professionalism of bloggers is elaborated. Abstract: Outreach of internet has opened new horizons for the people who want quick and widespread dissemination of their ideas, and the tool to do so is blogging. Bloggers can broadly be classified into two groups: professional and non-professional bloggers. As for professional bloggers, there are many factors that influence individuals to opt this profession. This study, with the help of an online dataset, attempts to identify such factors. Data analysis was made by using decision tree algorithms, lazy learning algorithms and ensembling methods. Nearest-neighbour classifier (IB1) and RandomForest have results with 85% accuracy and 84.8% precision for classification. The proof of concept is provided for result validation. The causes behind the varying performance of algorithms are elaborated. The factors that influence a blogger to behave professionally are identified based on the classifier with the best results. Graphical abstract: