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Comparing deep learning architectures for sentiment analysis on drug reviews.

Cristóbal Colón-Ruiz1, Isabel Segura-Bedmar1

  • 1Computer Science Department, University Carlos III of Madrid, Avenida de la Universidad 30, 28911, Leganés, Madrid, Spain.

Journal of Biomedical Informatics
|August 21, 2020
PubMed
Summary

Deep learning models like BERT and CNNs were compared for analyzing drug reviews. BERT achieved the best sentiment analysis results but required significant training time, while CNNs offered a faster alternative with acceptable performance.

Keywords:
Bidirectional Encoder Representations from TransformersConvolutional neural networkDeep learningLong short-term memoryMulti-class text classificationSentiment analysis

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Pharmacovigilance

Background:

  • Sentiment analysis of online user reviews is crucial for understanding drug effectiveness and side effects.
  • Deep learning techniques have advanced NLP tasks, yet their application to drug review sentiment analysis is underexplored.

Purpose of the Study:

  • To benchmark various deep learning architectures for sentiment analysis of drug reviews.
  • To evaluate the performance of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT) models.

Main Methods:

  • Compared CNN and LSTM recurrent neural networks, including model combinations and pre-trained word embeddings.
  • Explored BERT with Bi-LSTM for drug review sentiment analysis, assessing performance and training time.

Main Results:

  • BERT models achieved the highest accuracy in sentiment analysis of drug reviews.
  • CNN models provided acceptable results with substantially lower training times compared to BERT.

Conclusions:

  • BERT is effective for drug review sentiment analysis but computationally intensive.
  • CNNs present a practical alternative for sentiment analysis when training time is a constraint.