Learning implicit sentiments in Alzheimer's disease recognition with contextual attention features.

Area of Science:

• Computational linguistics
• Artificial intelligence in healthcare
• Neuroscience

Background:

• Alzheimer's disease (AD) diagnosis is challenging due to the subtle emotional cues within patient transcripts.
• Existing neural network models overlook the implicit sentiments crucial for AD detection.
• Implicit emotion classification at the document level is key for analyzing AD-related language.

Purpose of the Study:

• To develop a novel model for Alzheimer's disease diagnosis by analyzing implicit sentiments in transcripts.
• To improve the accuracy and effectiveness of AD detection using language-based analysis.
• To address the limitations of current neural network approaches in capturing subtle emotional nuances.

Main Methods:

• A two-level attention mechanism was employed to identify deep semantic information at word and sentence levels.
• Document representation was constructed by selectively aggregating important words into sentence vectors and sentences into document vectors.
• A supervised fuzzy implicit emotion classification approach was utilized for document-level analysis.

Main Results:

• The proposed model achieved a leading accuracy of 91.6% on the annotated Pitt corpora.
• The method demonstrated effectiveness in learning and representing implicit sentiment from transcripts.
• Attention layers successfully identified and selected informative words and sentences.

Conclusions:

• The developed model offers a promising approach for Alzheimer's disease diagnosis through implicit sentiment analysis of transcripts.
• The attention mechanism provides qualitative insights into informative linguistic features for AD.
• This method offers valuable inspiration for advancing AD diagnosis using linguistic and sentiment analysis.