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Feature based Summarizing and Ranking from Customer Reviews
http://hdl.handle.net/20.500.12678/0000003021
http://hdl.handle.net/20.500.12678/0000003021664b601a-4c47-4ff8-90fc-3dd9d3531a5d
46739031-fc2d-4678-9eb7-50d3fea49d70
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Feature based Summarizing and Ranking from Customer Reviews.pdf (375 Kb)
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Publication type | ||||||
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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 |