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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (5): 51-58    DOI: 10.11925/infotech.2096-3467.2020.1170
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A Semi-Supervised Sentiment Analysis Method for Chinese Based on Multi-Level Data Augmentation
Liu Tong,Liu Chen,Ni Weijian()
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
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Abstract  

[Objective] This paper designs a semi-supervised model for sentiment analysis based on multi-level data augmentation, aiming to generate high-quality labeled data for natural language processing in Chinese. [Methods] First, we collected large amount of unlabeled data with the help of simple data enhancement and reverse translation of text enhancement techniques. Then, we extracted the data signals of unlabeled samples by calculating their consistency norms. Third, we calculated the pseudo-label of the weakly enhanced samples, and constructed the supervised training signal from the strongly enhanced sample together with the pseudo-label. Finally, we set confidence threshold for the model to generate prediction results. [Results] We examined the proposed model with three publicly available datasets for sentiment analysis. With only 1 000 labeled documents from the Waimai and Weibo datasets, the performance of our model was 2.311% and 6.726% better than those of the BERT. [Limitations] We did not evaluate the model’s performance with vertical domain datasets. [Conclusions] The proposed method fully utilizes the information of unlabeled samples to address the issue of insufficient labeled data, and shows strong predicting stability.

Key wordsSentiment Analysis      Semi-Supervised Learning      Consistency Regularity      Data Augmentation     
Received: 27 November 2020      Published: 27 May 2021
ZTFLH:  TP393  
Fund:The work is supported by the National Natural Science Foundation of China(71704096);The work is supported by the National Natural Science Foundation of China(61602278);the Qingdao Social Science Planning Project(QDSKL2001117)
Corresponding Authors: Ni Weijian     E-mail: niweijian@gmail.com

Cite this article:

Liu Tong,Liu Chen,Ni Weijian. A Semi-Supervised Sentiment Analysis Method for Chinese Based on Multi-Level Data Augmentation. Data Analysis and Knowledge Discovery, 2021, 5(5): 51-58.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.1170     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I5/51

The Structure of SA-MLA
数据集 文档数量 情感类别数量
Waimai 11 988 2
Dmsc 27 389 5
Weibo 8 000 4
The Statistics of Three Datasets
方法 Waimai Dmsc Weibo
BERT 84.632% 51.662% 42.067%
TextCNN 81.715% 46.944% 41.483%
UDA 85.432% 44.986% 47.547%
SA-MLA 86.943% 45.319% 48.793%
The Prediction Effect of Each Method
方法 有标签数据 Waimai Dmsc Weibo
UDA 500 86.231% 41.837% 42.174%
1 000 85.432% 44.986% 47.547%
2 000 92.082% 46.039% 53.812%
SA-MLA 500 86.456% 42.413% 42.838%
1 000 86.943% 45.319% 48.793%
2 000 92.621% 46.825% 54.427%
The Performance of Semi-Supervised Methods Using Different Number of Labeled Documents
The Performance of UDA and SA-MLA Using Different Number of Unlabeled Samples
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