Abstract Among the number of formats of representing music, music sheets carrying a significant importance even though sheet music is not used by everyone. For musicians, composers and for those who are practicing sheet music, that is more convenient and the best materials as well. As defined by western musicians, it has grown and now widely available even in the internet.... Among the number of formats of representing music, music sheets carrying a significant importance even though sheet music is not used by everyone. For musicians, composers and for those who are practicing sheet music, that is more convenient and the best materials as well. As defined by western musicians, it has grown and now widely available even in the internet. As it become wider, the need of navigation and searching has become required because of the difficulties in browsing large amount of data. Online searching approaches different ways of querying such as text, audio etc. This paper is an investigation of the perception towards searching sheet music online in general. This includes a brief analysis of user needs and expectations towards the perception of online sheet music search contains an online survey, expert interviews and a discussion outlining the existing approaches and future work needed. Read More Read Less

Abstract In this work, we propose a framework suitable for multidimensional data mining based on tensor. A Tensor Data Model (TDM) provides us with high order data structure and naive description for information retrieval. Among others, we discuss multidimensional rule mining here. Generally, association rule mining (or extraction of association rules) concerns about co-related transaction records of single predicate, and hard to examine the... In this work, we propose a framework suitable for multidimensional data mining based on tensor. A Tensor Data Model (TDM) provides us with high order data structure and naive description for information retrieval. Among others, we discuss multidimensional rule mining here. Generally, association rule mining (or extraction of association rules) concerns about co-related transaction records of single predicate, and hard to examine the ones over multiple predicates since it takes heavy time- and space- complexities. Here we show TDM allows us to model several operations specific to multidimensional data mining yet to reduce amount of description. Read More Read Less

Abstract With the thriving of review sites on the Web, people have begun to express their opinions on a wide variety of products on several services. Sentiment analysis on entities of products thus becomes a rapid and effective way of assessing public opinion for business marketing. Sentiment analysis is the computational study of people’s opinions, attitude, emotion or appraisal pertaining to topics,... With the thriving of review sites on the Web, people have begun to express their opinions on a wide variety of products on several services. Sentiment analysis on entities of products thus becomes a rapid and effective way of assessing public opinion for business marketing. Sentiment analysis is the computational study of people’s opinions, attitude, emotion or appraisal pertaining to topics, objects, products, services, organizations, individuals, events or any attributes of them. Aspect detection in sentiment analysis helps customers to effectively navigate product information by zooming in on the product features they are interested in. In this paper we introduce Sentiment Miner, a novel unsupervised framework for aspect detection problem in a sentiment analysis system for online customer reviews. Sentiment Miner tries to detect aspects using a bootstrapping algorithm based on pointwise mutual information measure. The proposed framework considers multiword aspects as atomic terms by utilizing C-value method. Experimental results show that the Sentiment Miner improves the precision, recall and the F-score, and outperforms the state-of-the-art baseline approach. Read More Read Less