|Table of Contents|

Distance-Based Model for Target-Level Sentiment Analysis(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2021年04期
Page:
111-116
Research Field:
·计算机科学与技术·
Publishing date:

Info

Title:
Distance-Based Model for Target-Level Sentiment Analysis
Author(s):
Ma Xiaohui1; Ma Shangcai2; Yan Junya1; Chen Bo3
(1.Business College,Shanxi University,Taiyuan 030031,China)(2.Faculty of Information Management,Shanxi University of Finance and Economics,Taiyuan 030006,China)(3.School of Computer Science and Technology,Shandong University of Technology,Zibo 255020,China)
Keywords:
word embedding; distance information; convolutional neural network; target-level sentiment analysis
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2021.04.014
Abstract:
Target-level sentiment analysis is a fine-grained classification task. Aiming at the problems such as high cost of manually generated feature model,inability to capture context semantics,and long training time of recurrent neural network,a target-level sentiment analysis model based on distance is designed. The distance information between the target word and its neighbors is modeled through the distance perception window. Combined with the word embedding technology,the matrix-vector is established for the input text and distance information,respectively. The features are extracted using the convolutional neural network,and the semantic features of the text are combined with the distance features. Finally,experimental results achieved on a Sem 2014 dataset(Laptop and Restaurant)show that our approach achieves a significant improvement in the accuracy over the comparison models and has a shorter model training time. The research results have reference value for the applied research of target sentiment classification.

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Last Update: 2021-12-15