Amplayo, R. K., & Song, M. (2017). An adaptable fine-grained sentiment analysis for summarization of multiple short online reviews. Data & Knowledge Engineering, 110, 54-67.

https://doi.org/10.1016/j.datak.2017.03.009

Abstract
In this study, we present a novel method in generating summaries of multiple online reviews using a fine-grained sentiment extraction model for short texts, which is adaptable to different domains and languages. Adaptability of a model is defined as its ability to be easily modified and be usable on different domains and languages. This is important because of the diversity of domains and languages available. The fine-grained sentiment extraction model is divided into two methods: sentiment classification and aspect extraction. The sentiment classifier is built using a three-level classification approach, while the aspect extractor is built using extended biterm topic model (eBTM), an extension of LDA topic model for short texts. Overall, results show that the sentiment classifier outperforms baseline models and industry-standard classifiers while the aspect extractor outperforms other topic models in terms of aspect diversity and aspect extracting power. In addition, using the Naver movies dataset, we show that online review summarization can be effectively constructed using the proposed methods by comparing the results of our method and the results of a movie awards ceremony.

연구의의

본 연구는 온라인 커뮤니티에서 이루어지는 담론을 분석하기 위한 감성분석 기법을 개발하였음. 온라인상에서 이루어지는 이용자들의 커뮤니케이션 내용을 자동으로 요약하기 위한 것으로 온라인 시민사회의 커뮤니케이션 분석을 자동으로 시도하였음.