Comment Volume Prediction Using Neural Networks and Decision Trees
Kamaljot Singh, Ranjeet Kaur, Dinesh Kumar · international conference on Modelling and simulation · 2015
The leading treads towards social networking services had drawn massive public attention from last one and half decade. The amount of data that is uploaded to these social networking services is increasing day by day. So, their is massive requirement to study the highly dynamic behavior of users towards these services. This is a preliminary work to model the user patterns and to study the effectiveness of machine learning predictive modeling approaches on leading social networking service Facebook. We modeled the user comment patters, over the posts on Facebook Pages and predicted that how many comments a post is expected to receive in next H hrs. To automate the process, we developed a software prototype consisting of the crawler, Information extractor, information processor and knowledge discovery module. We used Neural Networks and Decision Trees, predictive modeling techniques on different data-set variants and evaluated them under Hits@10(custom measure), Area Under Curve, Evaluation Time and Mean Absolute error evaluation metrics. We concluded that the Decision trees performed better than the Neural Networks under light of all evaluation metrics.