Abstract: Finding semantic orientation and intensity of sentiment phrases and words is a substantial task of opinion mining. The problem is to give a score to each sentiment phrase, so that different expressions of opinions in different platforms, like social networks, can be processed. There have been several attempts to do this task, and this paper aims to score each sentiment phrase based on its occurrence in reviews with different overall ratings. The idea is that if a sentiment phrase occurs more in 5-starred reviews than in 3-starred ones, it should be more positive and more intense. The results support this idea. Each sentiment phrase in the corpus is given a score based on a weighted average of their frequency in reviews with different ratings. When a sentiment phrase gets a high score, it means it is more likely to be positive and more likely to be intense. And if a sentiment phrase gets a low score, it means that it is negative. This score sets the threshold of negativity and positivit

• Publication Date: 03-Aug-2014
• DOI: 10.15224/978-1-63248-005-7-98
• Views: 0
• Downloads: 0

Abstract: Clustering as one of the main branches of data mining, has gained an important place in the different applied fields. On the other hand, Peer-to-Peer (P2P) networks with features such as simplicity, low cost communication, and high availability resources, have gained a worldwide popularity in the present days. In P2P network, high volumes of data are distributed between dispersed data sources. In such distributed systems, sending data to a central site is not possible, due to processing, storage and handling costs. This issue makes the need for specific methods for distributed data clustering in P2P networks. This paper makes a technical review of distributed data clustering approaches in P2P network, to understand the research trends in this area.

• Publication Date: 03-Aug-2014
• DOI: 10.15224/978-1-63248-005-7-113
• Views: 0
• Downloads: 0