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Related Experiment Video

Updated: Jun 20, 2026

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study
07:30

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study

Published on: August 18, 2020

A cannabis use Reddit dataset for aspect-based sentiment analysis.

Tricia Park1, Sahithi Lakamana1, Aishwarya Alagappan2

  • 1Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA.

Data in Brief
|June 19, 2026
PubMed
Summary
This summary is machine-generated.

Researchers created a new dataset from Reddit to analyze sentiment in cannabis pain management discussions. This resource aids machine learning for health social media analysis.

Keywords:
Autoimmune rheumatic diseasesCannabisNatural language processingPain managementSentiment analysis

Related Experiment Videos

Last Updated: Jun 20, 2026

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study
07:30

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study

Published on: August 18, 2020

Area of Science:

  • Natural Language Processing
  • Public Health Informatics
  • Digital Epidemiology

Background:

  • Social media platforms like Reddit host valuable discussions on health topics.
  • Analyzing patient sentiment in health-related online discussions is crucial for understanding public health trends.
  • Cannabis is frequently discussed in relation to pain management, particularly within patient communities.

Purpose of the Study:

  • To develop a manually annotated dataset for sentiment analysis of cannabis-related discussions on Reddit.
  • To facilitate traditional and aspect-based sentiment analysis (ABSA) in the context of pain management.
  • To provide a resource for training and evaluating machine learning models in health social media analysis.

Main Methods:

  • Extracted Reddit posts from communities discussing autoimmune rheumatic diseases (ARDs).
  • Filtered posts using cannabis-related keywords and rule-based sentence segmentation.
  • Manually annotated 479 post-aspect pairs for traditional and aspect-based sentiment (positive, negative, neutral).
  • Assessed inter-annotator reliability using Krippendorff's alpha (traditional: 0.604, ABSA: 0.526).

Main Results:

  • A novel dataset of 479 post-aspect pairs for sentiment analysis of cannabis for pain management was created.
  • The dataset includes manual annotations for both traditional sentiment and aspect-based sentiment analysis.
  • Inter-annotator reliability scores indicate moderate agreement for the annotations.

Conclusions:

  • The developed dataset is a valuable resource for NLP, public health, and pain medicine research.
  • It supports the development and evaluation of machine learning models for ABSA in health-related social media.
  • The dataset enhances social media-based health monitoring and digital epidemiology efforts.