@article{(Open Science Index):https://publications.waset.org/pdf/10000873,
	  title     = {Feature-Based Summarizing and Ranking from Customer Reviews},
	  author    = {Dim En Nyaung and  Thin Lai Lai Thein},
	  country	= {},
	  institution	= {},
	  abstract     = {Due to the rapid increase of Internet, web opinion
sources dynamically emerge which is useful for both potential
customers and product manufacturers for prediction and decision
purposes. These are the user generated contents written in natural
languages and are unstructured-free-texts scheme. Therefore, opinion
mining techniques become popular to automatically process customer
reviews for extracting product features and user opinions expressed
over them. Since customer reviews may contain both opinionated and
factual sentences, a supervised machine learning technique applies
for subjectivity classification to improve the mining performance. In
this paper, we dedicate our work is the task of opinion
summarization. Therefore, product feature and opinion extraction is
critical to opinion summarization, because its effectiveness
significantly affects the identification of semantic relationships. The
polarity and numeric score of all the features are determined by
Senti-WordNet Lexicon. The problem of opinion summarization
refers how to relate the opinion words with respect to a certain
feature. Probabilistic based model of supervised learning will
improve the result that is more flexible and effective.
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {9},
	  number    = {3},
	  year      = {2015},
	  pages     = {734 - 739},
	  ee        = {https://publications.waset.org/pdf/10000873},
	  url   	= {https://publications.waset.org/vol/99},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 99, 2015},