Clause-level Negative-opinion Analysis for Classifying Reviews on Multiple Domains
Kazuhiro Akiyama, Mitsuzawa Kensuke, Kazuya Narita, Tadahiko Kumamoto, Akiyo Nadamoto · 2018
Today, vast amounts of reviews are posted on the internet. Businesses must extract negative opinions of products and services from reviews to improve their products and services. Still, some issues related to automatic extraction of negative sentiment must be addressed if they intend to use the information to improve their products and services. (1) Reviews are usually long texts. Finding only negative opinions for improvement is therefore time-consuming. (2) Many studies proposed about sentiment classification using machine learning. When we use the machine learning technique, we must prepare a lot of training data of the same product and services as test data. It is high cost. As described herein, we propose a clause-level sentiment classification method using Conditional Random Field (CRF) to address the issue (1). Also, we describe experiments of sentiment classification on reviews of multiple domains for the issue (2).