In:
網際網路技術學刊, Angle Publishing Co., Ltd., Vol. 24, No. 4 ( 2023-07), p. 1009-1016
Abstract:
〈p〉To address the problem of scarce low-resource sentiment analysis corpus nowadays, this paper proposes a sentence-level sentiment analysis resource conversion method HTL based on the syntactic-semantic knowledge of the low-resource language Uyghur to convert high-resource corpus to low-resource corpus. In the conversion process, a k-fold cross-filtering method is proposed to reduce the distortion of data samples, which is used to select high-quality samples for conversion; finally, the Uyghur sentiment analysis dataset USD is constructed; the Baseline of this dataset is verified under the LSTM model, and the accuracy and F1 values reach 81.07% and 81.13%, respectively, which can provide a reference for the construction of low-resource language corpus nowadays. The accuracy and F1 values reached 81.07% and 81.13%, respectively, which can provide a reference for the construction of today’s low-resource corpus. Meanwhile, this paper also proposes a sentiment analysis model based on logistic regression ensemble learning, SA-LREL, which combines the advantages of several lightweight network models such as TextCNN, RNN, and RCNN as the base model, and the meta-model is constructed using logistic regression functions for ensemble, and the accuracy and F1 values reach 82.17% and 81.86% respectively in the test set, and the experimental results show that the method can effectively improve the performance of Uyghur sentiment analysis task.〈/p〉
〈p〉 〈/p〉
Type of Medium:
Online Resource
ISSN:
1607-9264
,
1607-9264
Uniform Title:
Resource Construction and Ensemble Learning based Sentiment Analysis for the Low-resource Language Uyghur
DOI:
10.53106/160792642023072404018
Language:
Unknown
Publisher:
Angle Publishing Co., Ltd.
Publication Date:
2023
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