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  1. University of Information Technology
  2. Faculty of Computer Science

Feature based Summarizing and Ranking from Customer Reviews

http://hdl.handle.net/20.500.12678/0000003021
http://hdl.handle.net/20.500.12678/0000003021
664b601a-4c47-4ff8-90fc-3dd9d3531a5d
46739031-fc2d-4678-9eb7-50d3fea49d70
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Feature Feature based Summarizing and Ranking from Customer Reviews.pdf (375 Kb)
Publication type
Conference paper
Upload type
Publication
Title
Title Feature based Summarizing and Ranking from Customer Reviews
Language en
Publication date 2015-03-30
Authors
Dim En Nyaung
Thin Lai Lai Thein
Description
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.
Keywords
Opinion Mining, Opinion Summarization, Sentiment Analysis, Text Mining
Conference papers
ICITSE
29-30 March 2015
International Conference on Information Technology and Software Engineering
Singapore
https://panel.waset.org/conference/2015/03/singapore/program
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