Mr. Yuanhang Shi Profile

Yuanhang Shi

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 10 August 2023 Paper

An improved graphical convolutional network model based on interactive aspect-level sentiment analysis

Yuanhang Shi, Junjie Chen

Proceedings Volume 12759, 1275924 (2023) https://doi.org/10.1117/12.2686594

KEYWORDS: Performance modeling, Data modeling, Semantics, Sintering, Statistical modeling, Analytical research, Matrices, Feature extraction, Education and training, Neural networks

Read Abstract +

Aspect-level text sentiment analysis is a more complete and specific task of text sentiment analysis, which determines the sentiment information expressed by a text with respect to each aspect. In recent years, the use of syntactic dependency trees as well as sentence structure information for aspect-level sentiment analysis has become more common, but they often ignore the role of sentence semantics involved in sentiment analysis and fail to combine them. In this paper, we improve and propose an interactive graph convolutional network (SInterGCN) model incorporating syntactic semantic structure for text-level sentiment analysis tasks. Specifically, a dependency graph is first constructed for each sentence in the syntactic relational dependency tree. This is followed by combining the graph of structural relations with the associated semantic graph, which is used to capture key aspect-oriented and contextual aspects. Furthermore, in order to interactively extract dependencies between aspect words and other aspects, an aspect-oriented GCN is used to model the representation learned by the aspect-oriented GCN based on the inter-aspect graph. Thus, the model can be aware of important contexts and aspect words when interactively learning aspect-specific sentiment features. Experimental results on four relevant benchmark datasets demonstrate that our proposed model outperforms the original interactive approach, achieving superior performance and validating the effectiveness of our model.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years