@inproceedings{e724b0693cd548cab694cfb35bae8437,
title = "Opinion Mining on Product Review Based on PM-LDA",
abstract = "An updated framework based on LDA is provided in this paper to extract features from online user reviews which are in Chinese. This model is an extension of the LDA by introducing the concepts of multi-gram and part of speech into it and it is named PM-LDA. Through this model, features are generated as topics and topic labels can be generated as the sentence that has the max topic probability. Topics in PM-LDA are divided into two different types. The one is some general features such as product brand, color or producing area, and the other is those latent characteristics which customers may be more interested in. Part of speech is used to get the feature object and feature opinion separately, to make it more accurate. Several experiments are carried out to help to evaluate this model and it is indicated that this method has improved performance both in accuracy and the quality of the topic model itself.",
keywords = "Feature extraction, LDA, Opinion mining, Product review",
author = "Zhenni Wu and Jianyun Shang and Huaping Zhang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018 ; Conference date: 20-05-2018 Through 22-05-2018",
year = "2018",
month = sep,
day = "21",
doi = "10.1109/ACIIAsia.2018.8470390",
language = "English",
series = "2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018",
address = "United States",
}
