Semi-supervised distributed representations of documents for sentiment analysis.

Area of Science:

• Natural Language Processing
• Machine Learning
• Computational Linguistics

Background:

• Distributed representation learning models, like neural language models, have advanced beyond traditional vector space models for tasks such as sentiment analysis.
• Current models often struggle to capture text sentiment effectively due to their purely context-based learning objectives.

Purpose of the Study:

• To address the limitation of context-based objectives in reflecting text sentiment.
• To develop improved document embeddings that accurately represent sentiment for enhanced sentiment analysis.

Main Methods:

• Introduced a semi-supervised sentiment-discriminative objective to enhance document representation learning.
• Incorporated partial sentiment information of documents while preserving local structures from original objectives.
• Focused on sentiment relationships between neighboring documents to guide embedding learning.

Main Results:

• Document representations demonstrated enhanced sentiment class separation in visualization tasks on Amazon review datasets.
• The proposed method achieved superior sentiment prediction performance compared to existing methods on both Amazon and Yelp datasets.
• Validation through sentiment visualization and classification tasks confirmed the effectiveness of the new approach.

Conclusions:

• The proposed semi-supervised method effectively improves document embeddings for sentiment analysis by integrating sentiment-specific objectives.
• This approach offers a promising direction for developing more effective document representations for various discriminative tasks.
• The findings highlight the importance of sentiment-aware objectives in neural language models for natural language understanding.