Toward Aspect-Level Sentiment Modification Without Parallel Data

This article takes the lead to study aspect-level sentiment modification (ALSM) without parallel data. Given a sentence, the task of ALSM needs to reverse the sentiment with respect to the given aspect while preserving other content. The main challenge is reversing the sentiment of the given aspect without affecting the sentiments of other aspects in the sentences. To handle this problem, we propose a joint aspect-level sentiment modification (called JASM) model. JASM is a multitask system, which jointly trains two coupled modules: aspect-specific sentiment words extraction and aspect-level sentiment transformation. Besides, we propose a novel memory mechanism to learn aspect-aware sentiment representation and a gating mechanism to dynamically select aspect-aware sentiment information or content information for generating the next words. Experiments show that the proposed model substantially outperforms the compared methods in both aspect-level sentiment transformation and content preservation. For applications, we conduct data augmentation for aspect-based sentiment analysis (ABSA) through generating plausible training data with the trained ALSM model. Experiments show that data augmentation with generated data boosts the performance of a broad range of ABSA models.

Authors

• Qingnan Jiang
• Lei Chen
• Wei Zhao
• Min Yang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China

Keywords

• Sentiment analysis
• Analytical models
• Training data
• Data models
• Task analysis
• Intelligent systems
• Parallel Data
• Data Augmentation
• Range Of Models
• Range Of Performance
• Gating Mechanism
• Piece Of Text
• Sentiment Polarity
• Broad Range Of Models
• Target Entity
• Sentiment Words
• Seq2seq Model
• Preservation Of Content
• Training Set
• Time Step
• Attention Mechanism
• Learnable Parameters
• Hidden State
• Words In Sentences
• Input Sentence
• Original Sentence
• Automatic Evaluation
• Style Transfer