Đang chuẩn bị liên kết để tải về tài liệu:
Báo cáo khoa học: "Fine Granular Aspect Analysis using Latent Structural Models"

In this paper, we present a structural learning model for joint sentiment classification and aspect analysis of text at various levels of granularity. Our model aims to identify highly informative sentences that are aspect-specific in online custom reviews. The primary advantages of our model are two-fold: first, it performs document-level and sentence-level sentiment polarity classification jointly; second, it is able to find informative sentences that are closely related to some respects in a review, which may be helpful for aspect-level sentiment analysis such as aspect-oriented summarization. . | Fine Granular Aspect Analysis using Latent Structural Models Lei Fang1 and Minlie Huang2 State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing 100084 PR China. 1fang-l10@mails.tsinghua.edu.cn 2aihuang@tsinghua.edu.cn Abstract In this paper we present a structural learning model for joint sentiment classification and aspect analysis of text at various levels of granularity. Our model aims to identify highly informative sentences that are aspect-specific in online custom reviews. The primary advantages of our model are two-fold first it performs document-level and sentence-level sentiment polarity classification jointly second it is able to find informative sentences that are closely related to some respects in a review which may be helpful for aspect-level sentiment analysis such as aspect-oriented summarization. The proposed method was evaluated with 9 000 Chinese restaurant reviews. Preliminary experiments demonstrate that our model obtains promising performance. 1 Introduction Online reviews have been a major resource from which users may find opinions or comments on the products or services they want to consume. However users sometimes might be overwhelmed and not be able to read reviews one by one when facing a considerably large number of reviews and they may be not be satisfied by only being served with document-level reviews statistics that is the number of reviews with 1-star 2-star . respectively . Aspect-level review analysis may be alternative for addressing this issue as aspect-specific opinions may more clearly explicitly and completely describe the quality of a product from different properties. Our goal is to discover informative sentences that are consistent with the overall rating of a review and 333 simultaneously to perform sentiment analysis at aspect level. Notice that a review with a high rating say