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Sentiment classification with adversarial learning and attention mechanism
Author(s) -
Xu Yueshen,
Li Lei,
Gao Honghao,
Hei Lei,
Li Rui,
Wang Yihao
Publication year - 2021
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12329
Subject(s) - computer science , sentiment analysis , artificial intelligence , machine learning , adversarial system , word embedding , key (lock) , embedding , deep learning , mechanism (biology) , word (group theory) , task (project management) , multi task learning , mathematics , philosophy , geometry , computer security , management , epistemology , economics
Sentiment classification is a key task in sentiment analysis, reviews mining, and other text mining applications. Various models have been proposed to build sentiment classifiers, but the classification performances of some existing methods are not good enough. Meanwhile, as a subproblem of sentiment classification, positive and unlabeled learning (PU learning) problem widely exists in real‐world cases, but it has not been given enough attention. In this article, we aim to solve the two problems in one framework. We first build a model for traditional sentiment classification based on adversarial learning, attention mechanism, and long short‐term memory (LSTM) network. We further propose an enhanced adversarial learning method to tackle PU learning problem. We conducted extensive experiments in three real‐world datasets. The experimental results demonstrate that our models outperform the compared methods in both traditional sentiment classification problem and PU learning problem. Furthermore, we study the effect of our models on word embedding. Finally, we report and discuss the sensitivity of our models to parameters.