@article{2020:wei:featurele,
title = {Feature-Level Sentiment Analysis Based on Rules and Fine-Grained Domain Ontology},
year = {2020},
note = {Mining product reviews and sentiment analysis are of great
significance, whether for academic research purposes or optimizing
business strategies. We propose a feature-level sentiment analysis
framework based on rules parsing and fine-grained domain ontology for
Chinese reviews. Fine-grained ontology is used to describe synonymous
expressions of product features, which are reflected in word changes
in online reviews. First, a semiautomatic construction method is
developed by using Word2Vec for fine-grained ontology. Then,
feature-level sentiment analysis that combines rules parsing and the
fine-grained domain ontology is conducted to extract explicit and
implicit features from product reviews. Finally, the domain sentiment
dictionary and context sentiment dictionary are established to
identify sentiment polarities for the extracted feature-sentiment
combinations. An experiment is conducted on the basis of product
reviews crawled from Chinese e-commerce websites. The results
demonstrate the effectiveness of our approach.},
journal = {KO KNOWLEDGE ORGANIZATION},
pages = {105--121},
author = {Wei, Wie and Liu, Yi-Ping and Wei, Lei-Ru},
volume = {47},
number = {2}
}