Multi-granularity information enhancement text sentiment classification model integrating attention mechanism

Dong Wang; Pei Sheng Li

doi:10.1117/12.2639674

19 October 2022 Multi-granularity information enhancement text sentiment classification model integrating attention mechanism

Dong Wang, Pei Sheng Li

Author Affiliations +

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122945K (2022) https://doi.org/10.1117/12.2639674
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

Aiming at the sparse features of Chinese short texts, a multi-granularity information-enhanced text emotion classification model is proposed, which combines convolutional neural network and attention mechanism. First, fine-tune BERT model for specific tasks to obtain different granularity information contained in different layers, and express the text at sentence level as character-level vector. Then, using the feature of CNN with local semantic feature extraction, the multi-granularity local semantics of each layer of BERT is extracted with different scale convolutional kernels to obtain the multi-granularity local semantic representation under different layers. Considering that CLS at different levels of BERT contains global information at different levels of the text, it integrates and interacts with multi-granularity local semantic representation through attention mechanism to obtain multi-granularity deep semantic information. Finally, the final global semantic representation of BERT is fused with it to obtain the final text representation, which is input to the classifier for classification. The experimental comparison between this model and several models shows that the indexes of this model on two real data sets are obviously improved compared with the comparison model, which proves the effectiveness of this model.

Citation Download Citation

Dong Wang and Pei Sheng Li "Multi-granularity information enhancement text sentiment classification model integrating attention mechanism", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122945K (19 October 2022); https://doi.org/10.1117/12.2639674

ACCESS THE FULL ARTICLE

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: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Classification systems

Computer programming

Convolutional neural networks

Convolution

Feature extraction

Head

Show All Keywords

Keywords/Phrases

Search In:

Publication Years