An Opinion Mining By Manhattan Clustering Using Decision Tree Feature Selection
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 12
Abstract
In present era Opinion mining plays a major character in text mining applications, and people are more depend on the web for many actions like purchasing, investment, business markings, etc. These applications led to a young generation of companies and products meant for online market awareness, online content monitoring and reputation management. Opinion mining is a procedure in which it deals with impressions, sentiments, and the subjectivity of text. The scalable distance based Clustering Algorithm enables the identification of topics within discussions in web social networks and their development. The predefined set of clustering is valuable in web opinion clustering. Features are extracted from the data for classifying the sentiment. Feature selection has gained importance due to its contribution to save classification cost with regard to time and computation load. This paper is an attempt to review and evaluate the various techniques used for opinion mining analysis and the main focus is on feature selection for Opinion mining using decision tree based feature selection. The suggested method is evaluated using IMDb data set, and is compared with Principal Component Analysis (PCA).
Authors and Affiliations
P. Kavya Sri, K. C. Ravi Kumar, Dr. S. Anitha Reddy
Keywords
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