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 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.
