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A Framework for Feature Extraction and Ranking for Opinion Making from Online Reviews

  • Madeha ArifEmail author
  • Usman Qamar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)

Abstract

Opinion mining and its applications in product recommendation, business intelligence, targeted marketing etc. has got a lot of research attention to the area of data mining. Many researches have been conducted for improving opinion mining and various frameworks have been proposed. Still the improvement is in progress and more efficient and improved frameworks are being proposed. For opinion analysis from reviews, classifying them as positive and negative and then making future decisions about the product is very important and fascinating aspect of text mining. Many techniques fail to provide the coverage to the features of the product and are not very progressive in accurately classifying and ranking the reviews. In this paper, we have proposed a framework for opinion mining to process public reviews in Facebook comments. The features are ranked and clustered according to the similarity between them. Many methodologies fail at the point of finding which features are relatively similar and can be easily grouped together. This framework also retrieves reviews and summarizes them in a most efficient way providing coverage to the features.

Keywords

Opinion retrieval Features Rating Weight assignment POS (Parts Of Speech) 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer and Software Engineering, College of Electrical and Mechanical EngineeringNational University of Sciences and Technology (NUST)IslamabadPakistan

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