Lexical data augmentation for sentiment analysis

被引:22
|
作者
Xiang, Rong [1 ]
Chersoni, Emmanuele [1 ]
Lu, Qin [1 ]
Huang, Chu-Ren [1 ]
Li, Wenjie [1 ]
Long, Yunfei [2 ]
机构
[1] Hong Kong Polytech Univ, Hong Kong, Peoples R China
[2] Univ Essex, Colchester, Essex, England
关键词
Compilation and indexing terms; Copyright 2025 Elsevier Inc;
D O I
10.1002/asi.24493
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Machine learning methods, especially deep learning models, have achieved impressive performance in various natural language processing tasks including sentiment analysis. However, deep learning models are more demanding for training data. Data augmentation techniques are widely used to generate new instances based on modifications to existing data or relying on external knowledge bases to address annotated data scarcity, which hinders the full potential of machine learning techniques. This paper presents our work using part-of-speech (POS) focused lexical substitution for data augmentation (PLSDA) to enhance the performance of machine learning algorithms in sentiment analysis. We exploit POS information to identify words to be replaced and investigate different augmentation strategies to find semantically related substitutions when generating new instances. The choice of POS tags as well as a variety of strategies such as semantic-based substitution methods and sampling methods are discussed in detail. Performance evaluation focuses on the comparison between PLSDA and two previous lexical substitution-based data augmentation methods, one of which is thesaurus-based, and the other is lexicon manipulation based. Our approach is tested on five English sentiment analysis benchmarks: SST-2, MR, IMDB, Twitter, and AirRecord. Hyperparameters such as the candidate similarity threshold and number of newly generated instances are optimized. Results show that six classifiers (SVM, LSTM, BiLSTM-AT, bidirectional encoder representations from transformers [BERT], XLNet, and RoBERTa) trained with PLSDA achieve accuracy improvement of more than 0.6% comparing to two previous lexical substitution methods averaged on five benchmarks. Introducing POS constraint and well-designed augmentation strategies can improve the reliability of lexical data augmentation methods. Consequently, PLSDA significantly improves the performance of sentiment analysis algorithms.
引用
收藏
页码:1432 / 1447
页数:16
相关论文
共 50 条
  • [31] ACOUSTIC AND LEXICAL SENTIMENT ANALYSIS FOR CUSTOMER SERVICE CALLS
    Li, Bryan
    Dimitriadis, Dimitrios
    Stolcke, Andreas
    2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2019, : 5876 - 5880
  • [32] Sentiment Lexical Strength Enhanced Self-supervised Attention Learning for sentiment analysis
    Wang, Xi
    Fan, Mengmeng
    Kong, Mingming
    Pei, Zheng
    KNOWLEDGE-BASED SYSTEMS, 2022, 252
  • [33] Enhancing Minority Sentiment Classification in Gastronomy Tourism: A Hybrid Sentiment Analysis Framework With Data Augmentation, Feature Engineering and Business Intelligence
    Razali, Mohd Norhisham
    Manaf, Syaifulnizam Abdul
    Hanapi, Rozita Binti
    Salji, Mohd Rafiz
    Chiat, Lee Wen
    Nisar, Kashif
    IEEE ACCESS, 2024, 12 : 49387 - 49407
  • [34] Enhancing aspect-category sentiment analysis via syntactic data augmentation and knowledge enhancement
    Liu, Bin
    Lin, Tao
    Li, Ming
    KNOWLEDGE-BASED SYSTEMS, 2023, 264
  • [35] Data Augmentation Using BERT-Based Models for Aspect-Based Sentiment Analysis
    Hollander, Bron
    Frasincar, Flavius
    van der Knaap, Finn
    WEB ENGINEERING, ICWE 2024, 2024, 14629 : 115 - 122
  • [36] A Cross-Domain Generative Data Augmentation Framework for Aspect-Based Sentiment Analysis
    Xue, Jiawei
    Li, Yanhong
    Li, Zixuan
    Cui, Yue
    Zhang, Shaoqiang
    Wang, Shuqin
    ELECTRONICS, 2023, 12 (13)
  • [37] Use of Augmentation and Distant Supervision for Sentiment Analysis in Russian
    Golubev, Anton
    Loukachevitch, Natalia
    TEXT, SPEECH, AND DIALOGUE, TSD 2021, 2021, 12848 : 184 - 196
  • [39] A Lexical Upadating Algorithm for Sentiment Analysis on Chinese Movie Reviews
    Song, Yiwei
    Gu, Kaiwen
    Li, Huakang
    Sun, Guozi
    2017 FIFTH INTERNATIONAL CONFERENCE ON ADVANCED CLOUD AND BIG DATA (CBD), 2017, : 188 - 193
  • [40] Microblogging Sentiment Analysis with Lexical Based and Machine Learning Approaches
    Maharani, Warih
    2013 INTERNATIONAL CONFERENCE OF INFORMATION AND COMMUNICATION TECHNOLOGY (ICOICT), 2013, : 439 - 443