In Big Data analysis, the application of machine learning has proven to be a revolutionary. The systematic review of literature shows that research has been carried out on the domain of big data analytics particularly text analytics with the inclusion of machine learning approaches. This extensive survey deals with the data at hand that provides different ways and issues while combining the machine learning approaches with the text. During the course of the survey, various publications in the field of synchronous application of machine learning in text analytics were searched and studied. Classification framework is proposed as the contribution of machine learning in text analytics. A classification framework represented the various application areas to motivate researchers for future research on the application of two emerging technologies.
Social media is now not only limited to being a life event sharing platform, but it also has evolved as a monetary medium. Advertisements showing on popular videos may result in more sales conversion. So it is of utmost interest to predict the popularity of videos before uploading it on the platform. In this research article, we propose a deep learning algorithm to predict the popularity of YouTube videos. With the content and temporal features of the YouTube videos dataset, we use a novel stack of deep learning layers. We validate the approach with state-of-the-art methods and prove that the proposed complex stacked architecture gives more accurate and stable results. Results are also tested for short duration prediction with a different number of reference days after video publishing.
Recommendation making is an important part of the information and e-commerce ecosystem. Recommendation represent a powerful method that filter large amount of information to provide relevant choice to end users. To provide recommendations to the users, efficient and cost effective methods needs to be introduced. Collaborative filtering is an emerging technique used in making recommendations which makes use of filtering by data mining. This chapter presents a classification framework on the use of data mining techniques in collaborative filtering to extract the best recommendations to the users on the basis of their interests.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.