A Cannabis Use Reddit Dataset for Aspect-Based Sentiment Analysis

Abstract

Using publicly accessible Reddit posts, we developed a manually annotated dataset for traditional and aspect-based sentiment analysis (ABSA) of cannabis-related discussions in the context of pain management. The dataset consists of 479 post-aspect pairs extracted from specific Reddit communities associated with autoimmune rheumatic diseases (ARDs). We filtered posts using a structured list of cannabis-related terms and extracted context using rule-based sentence segmentation. Subsequently, we manually annotated each post for both traditional and aspect-specific sentiment (positive, negative, neutral). Inter-annotator reliability was assessed using Krippendorff’s ⍺, yielding ⍺ = 0.604 for traditional sentiment and ⍺ = 0.526 for aspect-based sentiment. The dataset offers valuable resources for training, benchmarking, and evaluating machine learning models for ABSA in health-related social media contexts. This dataset can support research in natural language processing, public health informatics, pain medicine, digital epidemiology, and social media-based health monitoring.

File structure

cannabis_reddit_absa.csv: Dataset containing Reddit post IDs, matched aspect terms, final traditional and aspect-based sentiment labels, and individual annotator labels for each post–aspect pair.

reddit_data_pipeline.ipynb: Jupyter Notebook containing functions for sentence segmentation with spaCy, and matching posts to the cannabis term list to generate post–aspect pairs for annotation.

cannabis_lexicon.csv: Complete list of cannabis-related terms used to filter and match relevant content in Reddit posts, including abbreviations, synonyms, and common spellings.

annotation_guidelines.pdf: Detailed instructions for annotators, including definitions of sentiment labels (neutral, positive, negative, blank), rules for assigning traditional vs. aspect-based labels, guidance for handling personal experience vs. general commentary, and illustrative examples.