2024-03-29T07:28:49Z
https://meral.edu.mm/oai
oai:meral.edu.mm:recid/2934
2021-12-13T00:30:09Z
1582963342780:1596102355557
user-uit
Improved Feature-based Summarizing and Mining from Hotel Customer Reviews
Dim En Nyaung
Thin Lai Lai Thein
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 to the main subtask
of opinion summarization. The task of 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 how intense the opinion is for both
positive and negative features. 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.
2015-02-06
http://hdl.handle.net/20.500.12678/0000002934
https://meral.edu.mm/records/2934