This unique compendium provides a novel research on how time influences the conversions of advertising and product recommendation in E-commerce. It proposes time-aware conversion prediction models to solve the problem - what products should be recommended for a given period to maximize conversion? The volume also presents a series of researches on how to build data-driven attribution models to allocate the time-sensitive contribution of advertisements to the conversion. This must-have reference text will be invaluable for researchers, professionals, academics and graduate students keen in databases and artificial intelligence.

This book provides a comprehensive introduction on opinion analysis for online reviews. It offers the newest research on opinion mining, including theories, algorithms and datasets. A new feature presentation method is highlighted for sentiment classification. Then, a three-phase framework for sentiment classification is proposed, where a set of sentiment classifiers are selected automatically to make predictions. Such predictions are integrated via ensemble learning. Finally, to solve the problem of combination explosion encountered, a greedy algorithm is devised to select the base classifiers.

This book provides a comprehensive introduction on opinion analysis for online reviews. It offers the newest research on opinion mining, including theories, algorithms and datasets. A new feature presentation method is highlighted for sentiment classification. Then, a three-phase framework for sentiment classification is proposed, where a set of sentiment classifiers are selected automatically to make predictions. Such predictions are integrated via ensemble learning. Finally, to solve the problem of combination explosion encountered, a greedy algorithm is devised to select the base classifiers.