Analysing Malaysian Public Discourse on Low Blood Pressure: A Sentiment Analysis and Big Data Approach

Abstract

This study analyses Malaysian public discourse on low blood pressure through big data analytics and sentiment analysis of social media interactions. Despite being frequently overshadowed by hypertension in public health discussions, low blood pressure significantly impacts quality of life and requires greater health literacy attention. Utilising sentiment analysis techniques on Facebook data from Public Health Malaysia, this research examined public discourse patterns and perceptions surrounding hypotension. The study analysed 100 Facebook comments using Google Colab and Python-based sentiment analysis tools. Data preprocessing involved translation from Bahasa Malaysia to English, text standardization, and sentiment classification. Findings revealed a nearly balanced sentiment distribution: neutral (28.8%), positive (36.5%), and negative (34.6%), with a mean sentiment score of 0.03 indicating marginally positive overall discourse. Temporal analysis identified peak engagement periods at 14:00-16:00 and 21:00, with sentiment fluctuations across different timeframes. Topic modelling uncovered key discourse themes including symptom recognition, medical consultations, daily management strategies, and clinical terminology understanding. Word cloud visualization highlighted frequently discussed terms such as “dizziness”, “blood pressure monitoring”, and “doctor consultations”. The findings confirm that social media platforms provide valuable insights into public health literacy and community health concerns. The balanced sentiment distribution suggests moderate awareness while underscoring the necessity for targeted health education initiatives. This research advances digital health communication strategies and offers evidence-based insights for healthcare providers to better address public concerns regarding low blood pressure through data-driven methodologies.