Shivakumar Vaithyanathan

TL;DR: In this paper, the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, was considered and three machine learning methods (Naive Bayes, maximum entropy classiflcation, and support vector machines) were employed.

TL;DR: This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.

TL;DR: This article used machine learning techniques such as Naive Bayes, maximum entropy classification, and support vector machines (SVM) for sentiment classification of movie reviews, and found that SVM outperformed human-produced baselines.

TL;DR: This paper proposes SystemML in which ML algorithms are expressed in a higher-level language and are compiled and executed in a MapReduce environment and describes and empirically evaluate a number of optimization strategies for efficiently executing these algorithms on Hadoop, an open-source mapReduce implementation.

TL;DR: This is the first paper to handle both imprecision and uncertainty in an OLAP setting and identify three natural query properties and use them to shed light on alternative query semantics.